File size: 65,404 Bytes
7cffc2b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 | {
"1512.03385": {
"arxivId": "1512.03385",
"title": "Deep Residual Learning for Image Recognition"
},
"1612.00593": {
"arxivId": "1612.00593",
"title": "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation"
},
"2005.12872": {
"arxivId": "2005.12872",
"title": "End-to-End Object Detection with Transformers"
},
"1706.02413": {
"arxivId": "1706.02413",
"title": "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space"
},
"1903.11027": {
"arxivId": "1903.11027",
"title": "nuScenes: A Multimodal Dataset for Autonomous Driving"
},
"2010.04159": {
"arxivId": "2010.04159",
"title": "Deformable DETR: Deformable Transformers for End-to-End Object Detection"
},
"1711.06396": {
"arxivId": "1711.06396",
"title": "VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection"
},
"1812.05784": {
"arxivId": "1812.05784",
"title": "PointPillars: Fast Encoders for Object Detection From Point Clouds"
},
"1611.07759": {
"arxivId": "1611.07759",
"title": "Multi-view 3D Object Detection Network for Autonomous Driving"
},
"2001.05566": {
"arxivId": "2001.05566",
"title": "Image Segmentation Using Deep Learning: A Survey"
},
"1904.08189": {
"arxivId": "1904.08189",
"title": "CenterNet: Keypoint Triplets for Object Detection"
},
"1912.04838": {
"arxivId": "1912.04838",
"title": "Scalability in Perception for Autonomous Driving: Waymo Open Dataset"
},
"1812.04244": {
"arxivId": "1812.04244",
"title": "PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud"
},
"1711.08488": {
"arxivId": "1711.08488",
"title": "Frustum PointNets for 3D Object Detection from RGB-D Data"
},
"1904.12848": {
"arxivId": "1904.12848",
"title": "Unsupervised Data Augmentation for Consistency Training"
},
"2012.12556": {
"arxivId": "2012.12556",
"title": "A Survey on Vision Transformer"
},
"1912.12033": {
"arxivId": "1912.12033",
"title": "Deep Learning for 3D Point Clouds: A Survey"
},
"1712.02294": {
"arxivId": "1712.02294",
"title": "Joint 3D Proposal Generation and Object Detection from View Aggregation"
},
"1911.02620": {
"arxivId": "1911.02620",
"title": "Argoverse: 3D Tracking and Forecasting With Rich Maps"
},
"2203.17270": {
"arxivId": "2203.17270",
"title": "BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers"
},
"1612.00496": {
"arxivId": "1612.00496",
"title": "3D Bounding Box Estimation Using Deep Learning and Geometry"
},
"1605.07648": {
"arxivId": "1605.07648",
"title": "FractalNet: Ultra-Deep Neural Networks without Residuals"
},
"1902.07830": {
"arxivId": "1902.07830",
"title": "Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges"
},
"2008.05711": {
"arxivId": "2008.05711",
"title": "Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D"
},
"2012.10992": {
"arxivId": "2012.10992",
"title": "Deep Continuous Fusion for Multi-sensor 3D Object Detection"
},
"1907.10471": {
"arxivId": "1907.10471",
"title": "STD: Sparse-to-Dense 3D Object Detector for Point Cloud"
},
"1911.10150": {
"arxivId": "1911.10150",
"title": "PointPainting: Sequential Fusion for 3D Object Detection"
},
"2205.13542": {
"arxivId": "2205.13542",
"title": "BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation"
},
"2003.01251": {
"arxivId": "2003.01251",
"title": "Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud"
},
"2104.11892": {
"arxivId": "2104.11892",
"title": "A Survey of Modern Deep Learning based Object Detection Models"
},
"1711.10871": {
"arxivId": "1711.10871",
"title": "PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation"
},
"2110.06922": {
"arxivId": "2110.06922",
"title": "DETR3D: 3d object detection from multi-view images via 3d-to-2d queries"
},
"2002.12478": {
"arxivId": "2002.12478",
"title": "Time Series Data Augmentation for Deep Learning: A Survey"
},
"2101.09671": {
"arxivId": "2101.09671",
"title": "Pruning and Quantization for Deep Neural Network Acceleration: A Survey"
},
"1609.06666": {
"arxivId": "1609.06666",
"title": "Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks"
},
"2112.11790": {
"arxivId": "2112.11790",
"title": "BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View"
},
"1906.11172": {
"arxivId": "1906.11172",
"title": "Learning Data Augmentation Strategies for Object Detection"
},
"2203.11496": {
"arxivId": "2203.11496",
"title": "TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers"
},
"1903.01864": {
"arxivId": "1903.01864",
"title": "Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal"
},
"1703.07570": {
"arxivId": "1703.07570",
"title": "Deep MANTA: A Coarse-to-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis from Monocular Image"
},
"2103.01100": {
"arxivId": "2103.01100",
"title": "Categorical Depth Distribution Network for Monocular 3D Object Detection"
},
"2006.06830": {
"arxivId": "2006.06830",
"title": "Data Augmentation for Graph Neural Networks"
},
"2004.12636": {
"arxivId": "2004.12636",
"title": "3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection"
},
"1811.08188": {
"arxivId": "1811.08188",
"title": "Orthographic Feature Transform for Monocular 3D Object Detection"
},
"1612.02297": {
"arxivId": "1612.02297",
"title": "Spatially Adaptive Computation Time for Residual Networks"
},
"1904.01649": {
"arxivId": "1904.01649",
"title": "MVX-Net: Multimodal VoxelNet for 3D Object Detection"
},
"2004.05224": {
"arxivId": "2004.05224",
"title": "Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review"
},
"2007.08856": {
"arxivId": "2007.08856",
"title": "EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection"
},
"2008.07519": {
"arxivId": "2008.07519",
"title": "V2VNet: Vehicle-to-vehicle communication for joint perception and prediction"
},
"2102.04803": {
"arxivId": "2102.04803",
"title": "DetCo: Unsupervised Contrastive Learning for Object Detection"
},
"2009.00784": {
"arxivId": "2009.00784",
"title": "CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection"
},
"1604.04693": {
"arxivId": "1604.04693",
"title": "Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection"
},
"2205.13790": {
"arxivId": "2205.13790",
"title": "BEVFusion: A simple and robust lidar-camera fusion framework"
},
"1806.01963": {
"arxivId": "1806.01963",
"title": "MILD\u2010Net: Minimal information loss dilated network for gland instance segmentation in colon histology images"
},
"2203.10638": {
"arxivId": "2203.10638",
"title": "V2X-ViT: Vehicle-to-everything cooperative perception with vision transformer"
},
"2203.08195": {
"arxivId": "2203.08195",
"title": "DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection"
},
"2106.10823": {
"arxivId": "2106.10823",
"title": "3D Object Detection for Autonomous Driving: A Survey"
},
"1703.02140": {
"arxivId": "1703.02140",
"title": "Information loss"
},
"2106.11037": {
"arxivId": "2106.11037",
"title": "One Million Scenes for Autonomous Driving: ONCE Dataset"
},
"1903.01568": {
"arxivId": "1903.01568",
"title": "The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes"
},
"2206.00630": {
"arxivId": "2206.00630",
"title": "Unifying Voxel-based Representation with Transformer for 3D Object Detection"
},
"2111.06881": {
"arxivId": "2111.06881",
"title": "Multimodal Virtual Point 3D Detection"
},
"1904.07537": {
"arxivId": "1904.07537",
"title": "Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds"
},
"2203.10642": {
"arxivId": "2203.10642",
"title": "FUTR3D: A Unified Sensor Fusion Framework for 3D Detection"
},
"1904.08601": {
"arxivId": "1904.08601",
"title": "Deep Optics for Monocular Depth Estimation and 3D Object Detection"
},
"2111.00643": {
"arxivId": "2111.00643",
"title": "Learning distilled collaboration graph for multi-agent perception"
},
"1911.06084": {
"arxivId": "1911.06084",
"title": "PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module"
},
"1811.03818": {
"arxivId": "1811.03818",
"title": "RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement"
},
"2204.12463": {
"arxivId": "2204.12463",
"title": "Focal Sparse Convolutional Networks for 3D Object Detection"
},
"1912.12791": {
"arxivId": "1912.12791",
"title": "Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots"
},
"2207.02202": {
"arxivId": "2207.02202",
"title": "CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers"
},
"2209.12836": {
"arxivId": "2209.12836",
"title": "Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps"
},
"1812.05276": {
"arxivId": "1812.05276",
"title": "IPOD: Intensive Point-based Object Detector for Point Cloud"
},
"2112.12610": {
"arxivId": "2112.12610",
"title": "PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving"
},
"2203.09780": {
"arxivId": "2203.09780",
"title": "Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion"
},
"2107.07502": {
"arxivId": "2107.07502",
"title": "MultiBench: Multiscale Benchmarks for Multimodal Representation Learning"
},
"2108.06709": {
"arxivId": "2108.06709",
"title": "SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation"
},
"1909.07541": {
"arxivId": "1909.07541",
"title": "A*3D Dataset: Towards Autonomous Driving in Challenging Environments"
},
"2106.12449": {
"arxivId": "2106.12449",
"title": "FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection"
},
"1901.03446": {
"arxivId": "1901.03446",
"title": "Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors"
},
"1901.03360": {
"arxivId": "1901.03360",
"title": "Unsupervised Moving Object Detection via Contextual Information Separation"
},
"2104.03775": {
"arxivId": "2104.03775",
"title": "Geometry-based Distance Decomposition for Monocular 3D Object Detection"
},
"2103.16470": {
"arxivId": "2103.16470",
"title": "Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection"
},
"2103.12605": {
"arxivId": "2103.12605",
"title": "MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation"
},
"2105.13502": {
"arxivId": "2105.13502",
"title": "Unsupervised Domain Adaptation of Object Detectors: A Survey"
},
"2006.12671": {
"arxivId": "2006.12671",
"title": "AFDet: Anchor Free One Stage 3D Object Detection"
},
"1908.11069": {
"arxivId": "1908.11069",
"title": "StarNet: Targeted Computation for Object Detection in Point Clouds"
},
"2111.14382": {
"arxivId": "2111.14382",
"title": "VPFNet: Improving 3D Object Detection With Virtual Point Based LiDAR and Stereo Data Fusion"
},
"2106.04550": {
"arxivId": "2106.04550",
"title": "DETReg: Unsupervised Pretraining with Region Priors for Object Detection"
},
"2103.00236": {
"arxivId": "2103.00236",
"title": "Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection"
},
"1605.07716": {
"arxivId": "1605.07716",
"title": "Deeply-Fused Nets"
},
"2202.02703": {
"arxivId": "2202.02703",
"title": "Multi-modal sensor fusion for auto driving perception: A survey"
},
"2201.06493": {
"arxivId": "2201.06493",
"title": "AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection"
},
"2106.12735": {
"arxivId": "2106.12735",
"title": "Multi-Modal 3D Object Detection in Autonomous Driving: A Survey"
},
"2207.10316": {
"arxivId": "2207.10316",
"title": "AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection"
},
"2112.11088": {
"arxivId": "2112.11088",
"title": "EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection"
},
"2103.13164": {
"arxivId": "2103.13164",
"title": "M3DSSD: Monocular 3D Single Stage Object Detector"
},
"2208.03624": {
"arxivId": "2208.03624",
"title": "Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph"
},
"2205.15938": {
"arxivId": "2205.15938",
"title": "Voxel Field Fusion for 3D Object Detection"
},
"2206.09474": {
"arxivId": "2206.09474",
"title": "3D Object Detection for Autonomous Driving: A Comprehensive Survey"
},
"1808.04285": {
"arxivId": "1808.04285",
"title": "Unsupervised Hard Example Mining from Videos for Improved Object Detection"
},
"2009.04554": {
"arxivId": "2009.04554",
"title": "RoIFusion: 3D Object Detection From LiDAR and Vision"
},
"2210.01391": {
"arxivId": "2210.01391",
"title": "Bridged Transformer for Vision and Point Cloud 3D Object Detection"
},
"2011.14589": {
"arxivId": "2011.14589",
"title": "Monocular 3D Object Detection With Sequential Feature Association and Depth Hint Augmentation"
},
"1909.04163": {
"arxivId": "1909.04163",
"title": "MLOD: A multi-view 3D object detection based on robust feature fusion method"
},
"2008.10436": {
"arxivId": "2008.10436",
"title": "Cross-Modality 3D Object Detection"
},
"2011.01404": {
"arxivId": "2011.01404",
"title": "Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion"
},
"1911.03576": {
"arxivId": "1911.03576",
"title": "PatchNet: Hierarchical Deep Learning-Based Stable Patch Identification for the Linux Kernel"
},
"2009.10945": {
"arxivId": "2009.10945",
"title": "MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion"
},
"1907.06777": {
"arxivId": "1907.06777",
"title": "Improving 3D Object Detection for Pedestrians with Virtual Multi-View Synthesis Orientation Estimation"
},
"2012.02938": {
"arxivId": "2012.02938",
"title": "Cirrus: A Long-range Bi-pattern LiDAR Dataset"
},
"2009.12276": {
"arxivId": "2009.12276",
"title": "SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation"
},
"2011.00652": {
"arxivId": "2011.00652",
"title": "Multi-View Adaptive Fusion Network for 3D Object Detection"
},
"2210.04801": {
"arxivId": "2210.04801",
"title": "4D Unsupervised Object Discovery"
},
"1506.01497": {
"arxivId": "1506.01497",
"title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
},
"1405.0312": {
"arxivId": "1405.0312",
"title": "Microsoft COCO: Common Objects in Context"
},
"1506.02640": {
"arxivId": "1506.02640",
"title": "You Only Look Once: Unified, Real-Time Object Detection"
},
"1311.2524": {
"arxivId": "1311.2524",
"title": "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation"
},
"1703.06870": {
"arxivId": "1703.06870",
"title": "Mask R-CNN"
},
"1504.08083": {
"arxivId": "1504.08083",
"title": "Fast R-CNN"
},
"2207.02696": {
"arxivId": "2207.02696",
"title": "YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors"
},
"1911.09070": {
"arxivId": "1911.09070",
"title": "EfficientDet: Scalable and Efficient Object Detection"
},
"1807.05511": {
"arxivId": "1807.05511",
"title": "Object Detection With Deep Learning: A Review"
},
"1812.07179": {
"arxivId": "1812.07179",
"title": "Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving"
},
"1907.09408": {
"arxivId": "1907.09408",
"title": "A Survey of Deep Learning-Based Object Detection"
},
"2108.05699": {
"arxivId": "2108.05699",
"title": "Oriented R-CNN for Object Detection"
},
"1902.09738": {
"arxivId": "1902.09738",
"title": "Stereo R-CNN Based 3D Object Detection for Autonomous Driving"
},
"2206.10092": {
"arxivId": "2206.10092",
"title": "BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection"
},
"1907.06038": {
"arxivId": "1907.06038",
"title": "M3D-RPN: Monocular 3D Region Proposal Network for Object Detection"
},
"1905.12365": {
"arxivId": "1905.12365",
"title": "Disentangling monocular 3d object detection"
},
"2008.13535": {
"arxivId": "2008.13535",
"title": "DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems"
},
"1907.07484": {
"arxivId": "1907.07484",
"title": "Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming"
},
"1906.06310": {
"arxivId": "1906.06310",
"title": "Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving"
},
"1711.07264": {
"arxivId": "1711.07264",
"title": "Light-Head R-CNN: In Defense of Two-Stage Object Detector"
},
"1608.07711": {
"arxivId": "1608.07711",
"title": "3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection"
},
"1903.10955": {
"arxivId": "1903.10955",
"title": "GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving"
},
"2002.10111": {
"arxivId": "2002.10111",
"title": "SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation"
},
"1912.04799": {
"arxivId": "1912.04799",
"title": "Learning Depth-Guided Convolutions for Monocular 3D Object Detection"
},
"2108.06417": {
"arxivId": "2108.06417",
"title": "Is Pseudo-Lidar needed for Monocular 3D Object detection?"
},
"1903.11444": {
"arxivId": "1903.11444",
"title": "Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving"
},
"2206.01191": {
"arxivId": "2206.01191",
"title": "EfficientFormer: Vision Transformers at MobileNet Speed"
},
"1903.09847": {
"arxivId": "1903.09847",
"title": "Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud"
},
"2003.00504": {
"arxivId": "2003.00504",
"title": "MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships"
},
"2204.05575": {
"arxivId": "2204.05575",
"title": "DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection"
},
"2212.07784": {
"arxivId": "2212.07784",
"title": "RTMDet: An Empirical Study of Designing Real-Time Object Detectors"
},
"2107.13774": {
"arxivId": "2107.13774",
"title": "Geometry uncertainty projection network for monocular 3d object detection"
},
"2001.10117": {
"arxivId": "2001.10117",
"title": "Canadian Adverse Driving Conditions dataset"
},
"2001.03398": {
"arxivId": "2001.03398",
"title": "DSGN: Deep Stereo Geometry Network for 3D Object Detection"
},
"2004.03080": {
"arxivId": "2004.03080",
"title": "End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection"
},
"1904.12681": {
"arxivId": "1904.12681",
"title": "Deep Fitting Degree Scoring Network for Monocular 3D Object Detection"
},
"2206.15398": {
"arxivId": "2206.15398",
"title": "PolarFormer: Multi-camera 3D Object Detection with Polar Transformers"
},
"2006.16471": {
"arxivId": "2006.16471",
"title": "Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques"
},
"2102.00690": {
"arxivId": "2102.00690",
"title": "Ground-Aware Monocular 3D Object Detection for Autonomous Driving"
},
"2203.10981": {
"arxivId": "2203.10981",
"title": "MonoDTR: Monocular 3d object detection with depth-aware transformer"
},
"1906.01193": {
"arxivId": "1906.01193",
"title": "Triangulation Learning Network: From Monocular to Stereo 3D Object Detection"
},
"2106.15796": {
"arxivId": "2106.15796",
"title": "Monocular 3D Object Detection: An Extrinsic Parameter Free Approach"
},
"2203.10168": {
"arxivId": "2203.10168",
"title": "Boreas: A multi-season autonomous driving dataset"
},
"2112.04628": {
"arxivId": "2112.04628",
"title": "Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection"
},
"2004.03572": {
"arxivId": "2004.03572",
"title": "Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation"
},
"2108.08258": {
"arxivId": "2108.08258",
"title": "LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector"
},
"2203.03800": {
"arxivId": "2203.03800",
"title": "Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild"
},
"1905.09970": {
"arxivId": "1905.09970",
"title": "Shift R-CNN: Deep Monocular 3D Object Detection With Closed-Form Geometric Constraints"
},
"2108.05793": {
"arxivId": "2108.05793",
"title": "Progressive Coordinate Transforms for Monocular 3D Object Detection"
},
"2303.02314": {
"arxivId": "2303.02314",
"title": "Virtual Sparse Convolution for Multimodal 3D Object Detection"
},
"2003.00529": {
"arxivId": "2003.00529",
"title": "ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection"
},
"2103.09422": {
"arxivId": "2103.09422",
"title": "YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection"
},
"2203.02112": {
"arxivId": "2203.02112",
"title": "Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving"
},
"1909.07701": {
"arxivId": "1909.07701",
"title": "Task-Aware Monocular Depth Estimation for 3D Object Detection"
},
"2203.13310": {
"arxivId": "2203.13310",
"title": "MonoDETR: Depth-guided transformer for monocular 3d object detection"
},
"2203.08563": {
"arxivId": "2203.08563",
"title": "MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection"
},
"2003.05505": {
"arxivId": "2003.05505",
"title": "Confidence Guided Stereo 3D Object Detection with Split Depth Estimation"
},
"1904.08494": {
"arxivId": "1904.08494",
"title": "Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles"
},
"2206.07372": {
"arxivId": "2206.07372",
"title": "MonoGround: Detecting Monocular 3D Objects from the Ground"
},
"1809.06132": {
"arxivId": "1809.06132",
"title": "Real-Time Dense Mapping for Self-Driving Vehicles using Fisheye Cameras"
},
"2303.10209": {
"arxivId": "2303.10209",
"title": "CAPE: Camera View Position Embedding for Multi-View 3D Object Detection"
},
"2204.00754": {
"arxivId": "2204.00754",
"title": "Homography Loss for Monocular 3D Object Detection"
},
"2101.06594": {
"arxivId": "2101.06594",
"title": "PLUMENet: Efficient 3D Object Detection from Stereo Images"
},
"2303.17297": {
"arxivId": "2303.17297",
"title": "Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving"
},
"2112.01914": {
"arxivId": "2112.01914",
"title": "SGM3D: Stereo Guided Monocular 3D Object Detection"
},
"2211.01142": {
"arxivId": "2211.01142",
"title": "OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection"
},
"2104.05858": {
"arxivId": "2104.05858",
"title": "Exploring Geometric Consistency for Monocular 3D Object Detection"
},
"2108.09663": {
"arxivId": "2108.09663",
"title": "SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation"
},
"2006.13084": {
"arxivId": "2006.13084",
"title": "Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time"
},
"2007.09836": {
"arxivId": "2007.09836",
"title": "Object-Aware Centroid Voting for Monocular 3D Object Detection"
},
"2301.10766": {
"arxivId": "2301.10766",
"title": "On the Adversarial Robustness of Camera-based 3D Object Detection"
},
"2211.13529": {
"arxivId": "2211.13529",
"title": "3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection"
},
"2006.16007": {
"arxivId": "2006.16007",
"title": "MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time"
},
"1912.01703": {
"arxivId": "1912.01703",
"title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library"
},
"1803.08669": {
"arxivId": "1803.08669",
"title": "Pyramid Stereo Matching Network"
},
"2006.11275": {
"arxivId": "2006.11275",
"title": "Center-based 3D Object Detection and Tracking"
},
"1803.06184": {
"arxivId": "1803.06184",
"title": "The ApolloScape Open Dataset for Autonomous Driving and Its Application"
},
"2301.00493": {
"arxivId": "2301.00493",
"title": "Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting"
},
"2109.13410": {
"arxivId": "2109.13410",
"title": "KITTI-360: A novel dataset and benchmarks for urban scene understanding in 2D and 3D"
},
"1908.09492": {
"arxivId": "1908.09492",
"title": "Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection"
},
"2203.05625": {
"arxivId": "2203.05625",
"title": "PETR: Position Embedding Transformation for Multi-View 3D Object Detection"
},
"2004.06320": {
"arxivId": "2004.06320",
"title": "A2D2: Audi Autonomous Driving Dataset"
},
"2206.01256": {
"arxivId": "2206.01256",
"title": "PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images"
},
"2112.06375": {
"arxivId": "2112.06375",
"title": "Embracing Single Stride 3D Object Detector with Sparse Transformer"
},
"1908.04512": {
"arxivId": "1908.04512",
"title": "Interpolated Convolutional Networks for 3D Point Cloud Understanding"
},
"2203.10314": {
"arxivId": "2203.10314",
"title": "Voxel set transformer: A set-to-set approach to 3d object detection from point clouds"
},
"2106.01178": {
"arxivId": "2106.01178",
"title": "ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection"
},
"2112.02205": {
"arxivId": "2112.02205",
"title": "Behind the Curtain: Learning Occluded Shapes for 3D Object Detection"
},
"2112.09205": {
"arxivId": "2112.09205",
"title": "AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds"
},
"2209.05588": {
"arxivId": "2209.05588",
"title": "CenterFormer: Center-based Transformer for 3D Object Detection"
},
"2208.11112": {
"arxivId": "2208.11112",
"title": "DeepInteraction: 3D Object Detection via Modality Interaction"
},
"2201.01976": {
"arxivId": "2201.01976",
"title": "SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection"
},
"2103.17202": {
"arxivId": "2103.17202",
"title": "GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection"
},
"2203.13608": {
"arxivId": "2203.13608",
"title": "Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task"
},
"2209.09385": {
"arxivId": "2209.09385",
"title": "LidarMultiNet: Towards a Unified Multi-task Network for LiDAR Perception"
},
"2205.05979": {
"arxivId": "2205.05979",
"title": "MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection"
},
"2203.09704": {
"arxivId": "2203.09704",
"title": "VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention"
},
"2106.13381": {
"arxivId": "2106.13381",
"title": "To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels"
},
"2204.06527": {
"arxivId": "2204.06527",
"title": "A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research"
},
"2209.03102": {
"arxivId": "2209.03102",
"title": "MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection"
},
"2207.02466": {
"arxivId": "2207.02466",
"title": "GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation"
},
"2203.00871": {
"arxivId": "2203.00871",
"title": "Dense Voxel Fusion for 3D Object Detection"
},
"2207.09332": {
"arxivId": "2207.09332",
"title": "Rethinking IoU-based Optimization for Single-stage 3D Object Detection"
},
"2106.02781": {
"arxivId": "2106.02781",
"title": "IPS300+: a Challenging Multimodal Dataset for Intersection Perception System"
},
"2105.14370": {
"arxivId": "2105.14370",
"title": "BAAI-VANJEE Roadside Dataset: Towards the Connected Automated Vehicle Highway technologies in Challenging Environments of China"
},
"1706.03762": {
"arxivId": "1706.03762",
"title": "Attention is All you Need"
},
"1505.04597": {
"arxivId": "1505.04597",
"title": "U-Net: Convolutional Networks for Biomedical Image Segmentation"
},
"1512.02325": {
"arxivId": "1512.02325",
"title": "SSD: Single Shot MultiBox Detector"
},
"1609.02907": {
"arxivId": "1609.02907",
"title": "Semi-Supervised Classification with Graph Convolutional Networks"
},
"1612.03144": {
"arxivId": "1612.03144",
"title": "Feature Pyramid Networks for Object Detection"
},
"1612.08242": {
"arxivId": "1612.08242",
"title": "YOLO9000: Better, Faster, Stronger"
},
"1706.02216": {
"arxivId": "1706.02216",
"title": "Inductive Representation Learning on Large Graphs"
},
"1406.4729": {
"arxivId": "1406.4729",
"title": "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition"
},
"1901.00596": {
"arxivId": "1901.00596",
"title": "A Comprehensive Survey on Graph Neural Networks"
},
"1801.07829": {
"arxivId": "1801.07829",
"title": "Dynamic Graph CNN for Learning on Point Clouds"
},
"1801.07791": {
"arxivId": "1801.07791",
"title": "PointCNN: Convolution On X-Transformed Points"
},
"1905.05055": {
"arxivId": "1905.05055",
"title": "Object Detection in 20 Years: A Survey"
},
"1806.02446": {
"arxivId": "1806.02446",
"title": "Deep Ordinal Regression Network for Monocular Depth Estimation"
},
"1912.13192": {
"arxivId": "1912.13192",
"title": "PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection"
},
"1902.06326": {
"arxivId": "1902.06326",
"title": "PIXOR: Real-time 3D Object Detection from Point Clouds"
},
"2002.10187": {
"arxivId": "2002.10187",
"title": "3DSSD: Point-Based 3D Single Stage Object Detector"
},
"1907.03670": {
"arxivId": "1907.03670",
"title": "From Points to Parts: 3D Object Detection From Point Cloud With Part-Aware and Part-Aggregation Network"
},
"1502.05082": {
"arxivId": "1502.05082",
"title": "What Makes for Effective Detection Proposals?"
},
"2012.15712": {
"arxivId": "2012.15712",
"title": "Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection"
},
"1511.02300": {
"arxivId": "1511.02300",
"title": "Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images"
},
"1907.03739": {
"arxivId": "1907.03739",
"title": "Point-Voxel CNN for Efficient 3D Deep Learning"
},
"1608.07916": {
"arxivId": "1608.07916",
"title": "Vehicle Detection from 3D Lidar Using Fully Convolutional Network"
},
"2012.12397": {
"arxivId": "2012.12397",
"title": "Multi-Task Multi-Sensor Fusion for 3D Object Detection"
},
"1811.02146": {
"arxivId": "1811.02146",
"title": "TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents"
},
"2109.02497": {
"arxivId": "2109.02497",
"title": "Voxel Transformer for 3D Object Detection"
},
"1908.02990": {
"arxivId": "1908.02990",
"title": "Fast Point R-CNN"
},
"1908.03851": {
"arxivId": "1908.03851",
"title": "IoU Loss for 2D/3D Object Detection"
},
"1910.06528": {
"arxivId": "1910.06528",
"title": "End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds"
},
"1912.05163": {
"arxivId": "1912.05163",
"title": "TANet: Robust 3D Object Detection from Point Clouds with Triple Attention"
},
"2104.09804": {
"arxivId": "2104.09804",
"title": "SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud"
},
"2012.03015": {
"arxivId": "2012.03015",
"title": "CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud"
},
"2104.02323": {
"arxivId": "2104.02323",
"title": "Objects are Different: Flexible Monocular 3D Object Detection"
},
"2004.00543": {
"arxivId": "2004.00543",
"title": "Physically Realizable Adversarial Examples for LiDAR Object Detection"
},
"2108.10723": {
"arxivId": "2108.10723",
"title": "Improving 3D Object Detection with Channel-wise Transformer"
},
"2103.16237": {
"arxivId": "2103.16237",
"title": "Delving into localization errors for monocular 3D object detection"
},
"2003.00186": {
"arxivId": "2003.00186",
"title": "HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection"
},
"1912.05992": {
"arxivId": "1912.05992",
"title": "IoU-aware Single-stage Object Detector for Accurate Localization"
},
"1912.04986": {
"arxivId": "1912.04986",
"title": "What You See is What You Get: Exploiting Visibility for 3D Object Detection"
},
"1804.05178": {
"arxivId": "1804.05178",
"title": "LiDAR and Camera Calibration Using Motions Estimated by Sensor Fusion Odometry"
},
"1912.00202": {
"arxivId": "1912.00202",
"title": "Relation Graph Network for 3D Object Detection in Point Clouds"
},
"1911.12236": {
"arxivId": "1911.12236",
"title": "PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement"
},
"2104.10330": {
"arxivId": "2104.10330",
"title": "BADET: Boundary-aware 3d object detection from point clouds"
},
"1907.05286": {
"arxivId": "1907.05286",
"title": "Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds"
},
"1906.05113": {
"arxivId": "1906.05113",
"title": "A survey of autonomous driving: Common practices and emerging technologies"
},
"2002.00444": {
"arxivId": "2002.00444",
"title": "Deep reinforcement learning for autonomous driving: A survey"
},
"2202.02980": {
"arxivId": "2202.02980",
"title": "3D Object Detection From Images for Autonomous Driving: A Survey"
},
"2312.03031": {
"arxivId": "2312.03031",
"title": "Is ego status all you need for open-loop end-to-end autonomous driving?"
},
"2306.16927": {
"arxivId": "2306.16927",
"title": "End-to-end autonomous driving: Challenges and frontiers"
},
"1711.03938": {
"arxivId": "1711.03938",
"title": "CARLA: An Open Urban Driving Simulator"
},
"2005.03778": {
"arxivId": "2005.03778",
"title": "LGSVL simulator: A high fidelity simulator for autonomous driving"
},
"1705.05065": {
"arxivId": "1705.05065",
"title": "AirSim: High-fidelity visual and physical simulation for autonomous vehicles"
},
"2304.14365": {
"arxivId": "2304.14365",
"title": "OCC3D: A large-scale 3D occupancy prediction benchmark for autonomous driving"
},
"2109.07644": {
"arxivId": "2109.07644",
"title": "OPV2V: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication"
},
"2202.08449": {
"arxivId": "2202.08449",
"title": "V2X-Sim: Multi-agent collaborative perception dataset and benchmark for autonomous driving"
},
"2403.01316": {
"arxivId": "2403.01316",
"title": "TUMTraf V2X cooperative perception dataset"
},
"1804.02767": {
"arxivId": "1804.02767",
"title": "YOLOv3: An incremental improvement"
},
"2104.10956": {
"arxivId": "2104.10956",
"title": "FCOS3D: Fully convolutional one-stage monocular 3d object detection"
},
"1904.08506": {
"arxivId": "1904.08506",
"title": "Adaptive hierarchical down-sampling for point cloud classification"
},
"2203.13394": {
"arxivId": "2203.13394",
"title": "Point2Seq: Detecting 3d objects as sequences"
},
"2303.11301": {
"arxivId": "2303.11301",
"title": "VoxelNext: Fully sparse voxelnet for 3d object detection and tracking"
},
"2403.15241": {
"arxivId": "2403.15241",
"title": "IS-Fusion: Instance-scene collaborative fusion for multimodal 3d object detection"
},
"2012.12395": {
"arxivId": "2012.12395",
"title": "Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net"
},
"2007.12392": {
"arxivId": "2007.12392",
"title": "An LSTM approach to temporal 3d object detection in lidar point clouds"
},
"2004.01389": {
"arxivId": "2004.01389",
"title": "Lidar-based online 3d video object detection with graph-based message passing and spatiotemporal transformer attention"
},
"2005.04255": {
"arxivId": "2005.04255",
"title": "STINet: Spatio-temporal-interactive network for pedestrian detection and trajectory prediction"
},
"2011.13628": {
"arxivId": "2011.13628",
"title": "Temporal-channel transformer for 3d lidar-based video object detection for autonomous driving"
},
"1811.10742": {
"arxivId": "1811.10742",
"title": "Joint monocular 3d vehicle detection and tracking"
},
"1803.01271": {
"arxivId": "1803.01271",
"title": "An empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
},
"2303.11926": {
"arxivId": "2303.11926",
"title": "Exploring object-centric temporal modeling for efficient multi-view 3d object detection"
},
"1904.10666": {
"arxivId": "1904.10666",
"title": "Segmenting the future"
},
"1907.11475": {
"arxivId": "1907.11475",
"title": "Single level feature-to-feature forecasting with deformable convolutions"
},
"2303.10552": {
"arxivId": "2303.10552",
"title": "Vehicle-infrastructure cooperative 3d object detection via feature flow prediction"
},
"2311.01682": {
"arxivId": "2311.01682",
"title": "Flow-based feature fusion for vehicle-infrastructure cooperative 3d object detection"
},
"2308.16896": {
"arxivId": "2308.16896",
"title": "PointOcc: Cylindrical tri-perspective view for point-based 3d semantic occupancy prediction"
},
"2011.09141": {
"arxivId": "2011.09141",
"title": "Semantic scene completion using local deep implicit functions on lidar data"
},
"2310.11239": {
"arxivId": "2310.11239",
"title": "Lidar-based 4d occupancy completion and forecasting"
},
"2112.00726": {
"arxivId": "2112.00726",
"title": "MonoScene: Monocular 3d semantic scene completion"
},
"2302.07817": {
"arxivId": "2302.07817",
"title": "Tri-perspective view for vision-based 3d semantic occupancy prediction"
},
"2306.02851": {
"arxivId": "2306.02851",
"title": "Scene as occupancy"
},
"2311.12754": {
"arxivId": "2311.12754",
"title": "SelfOcc: Self-supervised vision-based 3d occupancy prediction"
},
"2311.17663": {
"arxivId": "2311.17663",
"title": "Cam4DOcc: Benchmark for camera-only 4d occupancy forecasting in autonomous driving applications"
},
"2303.03991": {
"arxivId": "2303.03991",
"title": "OpenOccupancy: A large scale benchmark for surrounding semantic occupancy perception"
},
"1604.07316": {
"arxivId": "1604.07316",
"title": "End to end learning for self-driving cars"
},
"1904.04375": {
"arxivId": "1904.04375",
"title": "Controlling steering angle for cooperative self-driving vehicles utilizing cnn and lstm-based deep networks"
},
"1011.0686": {
"arxivId": "1011.0686",
"title": "A reduction of imitation learning and structured prediction to no-regret online learning"
},
"1912.12294": {
"arxivId": "1912.12294",
"title": "Learning by cheating"
},
"2106.06452": {
"arxivId": "2106.06452",
"title": "Keyframe-focused visual imitation learning"
},
"2110.14118": {
"arxivId": "2110.14118",
"title": "Object-aware regularization for addressing causal confusion in imitation learning"
},
"1707.06347": {
"arxivId": "1707.06347",
"title": "Proximal policy optimization algorithms"
},
"1509.02971": {
"arxivId": "1509.02971",
"title": "Continuous control with deep reinforcement learning"
},
"2008.05930": {
"arxivId": "2008.05930",
"title": "Perceive, predict, and plan: Safe motion planning through interpretable semantic representations"
},
"2101.06806": {
"arxivId": "2101.06806",
"title": "MP3: A unified model to map, perceive, predict and plan"
},
"2212.10156": {
"arxivId": "2212.10156",
"title": "Planning-oriented autonomous driving"
},
"2205.15997": {
"arxivId": "2205.15997",
"title": "TransFuser: Imitation with transformer-based sensor fusion for autonomous driving"
},
"2402.11502": {
"arxivId": "2402.11502",
"title": "GenAD: Generative end-to-end autonomous driving"
},
"2311.12320": {
"arxivId": "2311.12320",
"title": "A survey on multimodal large language models for autonomous driving"
},
"2309.05186": {
"arxivId": "2309.05186",
"title": "HiLM-D: Towards high-resolution understanding in multimodal large language models for autonomous driving"
},
"2309.05282": {
"arxivId": "2309.05282",
"title": "Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving"
},
"2307.07162": {
"arxivId": "2307.07162",
"title": "Drive like a human: Rethinking autonomous driving with large language models"
},
"2310.01957": {
"arxivId": "2310.01957",
"title": "Driving with LLMs: Fusing object-level vector modality for explainable autonomous driving"
},
"2403.04593": {
"arxivId": "2403.04593",
"title": "Embodied understanding of driving scenarios"
},
"2303.13560": {
"arxivId": "2303.13560",
"title": "Collaboration helps camera overtake lidar in 3d detection"
},
"2202.06689": {
"arxivId": "2202.06689",
"title": "CodeFill: Multi-token code completion by jointly learning from structure and naming sequences"
},
"2301.06262": {
"arxivId": "2301.06262",
"title": "Collaborative perception in autonomous driving: Methods, datasets, and challenges"
},
"2303.03595": {
"arxivId": "2303.03595",
"title": "LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross- Modal Fusion"
},
"2306.10013": {
"arxivId": "2306.10013",
"title": "PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation"
},
"1409.1556": {
"arxivId": "1409.1556",
"title": "Very Deep Convolutional Networks for Large-Scale Image Recognition"
},
"1605.06211": {
"arxivId": "1605.06211",
"title": "Fully convolutional networks for semantic segmentation"
},
"1608.06993": {
"arxivId": "1608.06993",
"title": "Densely Connected Convolutional Networks"
},
"1503.02531": {
"arxivId": "1503.02531",
"title": "Distilling the Knowledge in a Neural Network"
},
"1606.00915": {
"arxivId": "1606.00915",
"title": "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs"
},
"1404.7828": {
"arxivId": "1404.7828",
"title": "Deep learning in neural networks: An overview"
},
"1408.5882": {
"arxivId": "1408.5882",
"title": "Convolutional Neural Networks for Sentence Classification"
},
"1604.01685": {
"arxivId": "1604.01685",
"title": "The Cityscapes Dataset for Semantic Urban Scene Understanding"
},
"1711.07971": {
"arxivId": "1711.07971",
"title": "Non-local Neural Networks"
},
"1411.1792": {
"arxivId": "1411.1792",
"title": "How transferable are features in deep neural networks?"
},
"1806.09055": {
"arxivId": "1806.09055",
"title": "DARTS: Differentiable Architecture Search"
},
"1611.10012": {
"arxivId": "1611.10012",
"title": "Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors"
},
"1608.02192": {
"arxivId": "1608.02192",
"title": "Playing for Data: Ground Truth from Computer Games"
},
"1802.03601": {
"arxivId": "1802.03601",
"title": "Deep Visual Domain Adaptation: A Survey"
},
"1611.05009": {
"arxivId": "1611.05009",
"title": "OctNet: Learning Deep 3D Representations at High Resolutions"
},
"1904.09664": {
"arxivId": "1904.09664",
"title": "Deep Hough Voting for 3D Object Detection in Point Clouds"
},
"1605.06457": {
"arxivId": "1605.06457",
"title": "VirtualWorlds as Proxy for Multi-object Tracking Analysis"
},
"1703.07511": {
"arxivId": "1703.07511",
"title": "Deep Photo Style Transfer"
},
"2007.16100": {
"arxivId": "2007.16100",
"title": "Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution"
},
"2101.06742": {
"arxivId": "2101.06742",
"title": "Deep Parametric Continuous Convolutional Neural Networks"
},
"1611.08069": {
"arxivId": "1611.08069",
"title": "3D fully convolutional network for vehicle detection in point cloud"
},
"1807.00652": {
"arxivId": "1807.00652",
"title": "PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation"
},
"2012.11409": {
"arxivId": "2012.11409",
"title": "3D Object Detection with Pointformer"
},
"1809.07941": {
"arxivId": "1809.07941",
"title": "LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks"
},
"2203.17054": {
"arxivId": "2203.17054",
"title": "BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection"
},
"1810.10093": {
"arxivId": "1810.10093",
"title": "Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data"
},
"1805.01195": {
"arxivId": "1805.01195",
"title": "BirdNet: A 3D Object Detection Framework from LiDAR Information"
},
"2011.04841": {
"arxivId": "2011.04841",
"title": "CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection"
},
"1904.11621": {
"arxivId": "1904.11621",
"title": "Meta-Sim: Learning to Generate Synthetic Datasets"
},
"2205.02833": {
"arxivId": "2205.02833",
"title": "Cross-view Transformers for real-time Map-view Semantic Segmentation"
},
"2003.13402": {
"arxivId": "2003.13402",
"title": "Predicting Semantic Map Representations From Images Using Pyramid Occupancy Networks"
},
"1811.10247": {
"arxivId": "1811.10247",
"title": "MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization"
},
"2006.09348": {
"arxivId": "2006.09348",
"title": "LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World"
},
"2103.10039": {
"arxivId": "2103.10039",
"title": "RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection"
},
"2012.14176": {
"arxivId": "2012.14176",
"title": "Deep Visual Domain Adaptation"
},
"2010.09076": {
"arxivId": "2010.09076",
"title": "RADIATE: A Radar Dataset for Automotive Perception in Bad Weather"
},
"1511.03240": {
"arxivId": "1511.03240",
"title": "Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer"
},
"1901.10951": {
"arxivId": "1901.10951",
"title": "Distant Vehicle Detection Using Radar and Vision"
},
"1707.03167": {
"arxivId": "1707.03167",
"title": "RegNet: Multimodal sensor registration using deep neural networks"
},
"2004.00448": {
"arxivId": "2004.00448",
"title": "Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy"
},
"1902.03334": {
"arxivId": "1902.03334",
"title": "Photorealistic Image Synthesis for Object Instance Detection"
},
"1905.00526": {
"arxivId": "1905.00526",
"title": "RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles"
},
"1811.10800": {
"arxivId": "1811.10800",
"title": "Probabilistic Object Detection: Definition and Evaluation"
},
"2104.11896": {
"arxivId": "2104.11896",
"title": "M3DETR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers"
},
"2007.14366": {
"arxivId": "2007.14366",
"title": "RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects"
},
"2105.04619": {
"arxivId": "2105.04619",
"title": "Enhancing Photorealism Enhancement"
},
"1901.02237": {
"arxivId": "1901.02237",
"title": "3D Object Detection Using Scale Invariant and Feature Reweighting Networks"
},
"1909.07566": {
"arxivId": "1909.07566",
"title": "Object-Centric Stereo Matching for 3D Object Detection"
},
"2009.00206": {
"arxivId": "2009.00206",
"title": "RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation"
},
"2107.14391": {
"arxivId": "2107.14391",
"title": "From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection"
},
"2006.07864": {
"arxivId": "2006.07864",
"title": "Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection"
},
"2206.10555": {
"arxivId": "2206.10555",
"title": "Scaling up Kernels in 3D CNNs"
},
"2109.00892": {
"arxivId": "2109.00892",
"title": "KITTI-CARLA: a KITTI-like dataset generated by CARLA Simulator"
},
"2103.02093": {
"arxivId": "2103.02093",
"title": "Pseudo-labeling for Scalable 3D Object Detection"
},
"2103.16694": {
"arxivId": "2103.16694",
"title": "Geometric Unsupervised Domain Adaptation for Semantic Segmentation"
},
"2006.15505": {
"arxivId": "2006.15505",
"title": "1st Place Solution for Waymo Open Dataset Challenge - 3D Detection and Domain Adaptation"
},
"2012.12741": {
"arxivId": "2012.12741",
"title": "Multi-Modality Cut and Paste for 3D Object Detection"
},
"2003.00851": {
"arxivId": "2003.00851",
"title": "Deep Learning on Radar Centric 3D Object Detection"
},
"2107.02493": {
"arxivId": "2107.02493",
"title": "Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting"
},
"1709.07492": {
"arxivId": "1709.07492",
"title": "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image"
},
"1702.05374": {
"arxivId": "1702.05374",
"title": "Domain Adaptation for Visual Applications: A Comprehensive Survey"
},
"2301.06051": {
"arxivId": "2301.06051",
"title": "DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets"
},
"2212.05867": {
"arxivId": "2212.05867",
"title": "ALSO: Automotive Lidar Self-Supervision by Occupancy Estimation"
},
"2301.10222": {
"arxivId": "2301.10222",
"title": "RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving"
},
"2201.07706": {
"arxivId": "2201.07706",
"title": "Object Detection in Autonomous Vehicles: Status and Open Challenges"
},
"2304.00670": {
"arxivId": "2304.00670",
"title": "CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception"
},
"2308.07732": {
"arxivId": "2308.07732",
"title": "UniTR: A Unified and Efficient Multi-Modal Transformer for Bird\u2019s-Eye-View Representation"
},
"2010.15614": {
"arxivId": "2010.15614",
"title": "An Overview Of 3D Object Detection"
},
"2303.02203": {
"arxivId": "2303.02203",
"title": "X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection"
},
"2103.00550": {
"arxivId": "2103.00550",
"title": "A Survey on Deep Semi-Supervised Learning"
},
"2006.07529": {
"arxivId": "2006.07529",
"title": "Rethinking the Value of Labels for Improving Class-Imbalanced Learning"
},
"2102.00463": {
"arxivId": "2102.00463",
"title": "PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection"
},
"2006.14480": {
"arxivId": "2006.14480",
"title": "One Thousand and One Hours: Self-driving Motion Prediction Dataset"
},
"1802.00036": {
"arxivId": "1802.00036",
"title": "In Defense of Classical Image Processing: Fast Depth Completion on the CPU"
},
"2008.13719": {
"arxivId": "2008.13719",
"title": "RESA: Recurrent Feature-Shift Aggregator for Lane Detection"
},
"2106.04538": {
"arxivId": "2106.04538",
"title": "What Makes Multimodal Learning Better than Single (Provably)"
},
"2203.11089": {
"arxivId": "2203.11089",
"title": "PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark"
},
"1803.00387": {
"arxivId": "1803.00387",
"title": "A General Pipeline for 3D Detection of Vehicles"
},
"1904.01206": {
"arxivId": "1904.01206",
"title": "Progressive LiDAR adaptation for road detection"
},
"2004.02774": {
"arxivId": "2004.02774",
"title": "SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds"
},
"2207.12654": {
"arxivId": "2207.12654",
"title": "ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection"
},
"2207.12655": {
"arxivId": "2207.12655",
"title": "Semi-supervised 3D Object Detection with Proficient Teachers"
},
"2211.07171": {
"arxivId": "2211.07171",
"title": "Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection"
},
"2202.13589": {
"arxivId": "2202.13589",
"title": "Unsupervised Point Cloud Representation Learning With Deep Neural Networks: A Survey"
},
"1812.11478": {
"arxivId": "1812.11478",
"title": "DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification"
},
"2210.09615": {
"arxivId": "2210.09615",
"title": "Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection"
},
"2009.11345": {
"arxivId": "2009.11345",
"title": "TDR-OBCA: A Reliable Planner for Autonomous Driving in Free-Space Environment"
},
"1505.00256": {
"arxivId": "1505.00256",
"title": "DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving"
},
"1803.03243": {
"arxivId": "1803.03243",
"title": "Domain Adaptive Faster R-CNN for Object Detection in the Wild"
},
"1708.07819": {
"arxivId": "1708.07819",
"title": "Semantic Foggy Scene Understanding with Synthetic Data"
},
"1609.07769": {
"arxivId": "1609.07769",
"title": "Deep Joint Rain Detection and Removal from a Single Image"
},
"1612.02649": {
"arxivId": "1612.02649",
"title": "FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation"
},
"1901.09221": {
"arxivId": "1901.09221",
"title": "Progressive Image Deraining Networks: A Better and Simpler Baseline"
},
"1711.10098": {
"arxivId": "1711.10098",
"title": "Attentive Generative Adversarial Network for Raindrop Removal from A Single Image"
},
"1904.01538": {
"arxivId": "1904.01538",
"title": "Spatial Attentive Single-Image Deraining With a High Quality Real Rain Dataset"
},
"2004.08467": {
"arxivId": "2004.08467",
"title": "Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems"
},
"1909.01300": {
"arxivId": "1909.01300",
"title": "The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset"
},
"1903.08701": {
"arxivId": "1903.08701",
"title": "LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving"
},
"2003.14338": {
"arxivId": "2003.14338",
"title": "TartanAir: A Dataset to Push the Limits of Visual SLAM"
},
"1904.01690": {
"arxivId": "1904.01690",
"title": "Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction"
},
"1912.03874": {
"arxivId": "1912.03874",
"title": "CNN-Based Lidar Point Cloud De-Noising in Adverse Weather"
},
"1904.11466": {
"arxivId": "1904.11466",
"title": "Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation"
},
"2009.03683": {
"arxivId": "2009.03683",
"title": "Rain Rendering for Evaluating and Improving Robustness to Bad Weather"
},
"2003.06660": {
"arxivId": "2003.06660",
"title": "What Happens for a ToF LiDAR in Fog?"
},
"1910.05395": {
"arxivId": "1910.05395",
"title": "FuseMODNet: Real-Time Camera and LiDAR Based Moving Object Detection for Robust Low-Light Autonomous Driving"
},
"2009.02672": {
"arxivId": "2009.02672",
"title": "Approaches, Challenges, and Applications for Deep Visual Odometry: Toward Complicated and Emerging Areas"
},
"2007.13281": {
"arxivId": "2007.13281",
"title": "The Adaptability and Challenges of Autonomous Vehicles to Pedestrians in Urban China"
},
"1910.03997": {
"arxivId": "1910.03997",
"title": "Semantic Understanding of Foggy Scenes with Purely Synthetic Data"
},
"1807.02323": {
"arxivId": "1807.02323",
"title": "Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions"
},
"2106.14087": {
"arxivId": "2106.14087",
"title": "Radar Voxel Fusion for 3D Object Detection"
},
"2103.11071": {
"arxivId": "2103.11071",
"title": "Stereo CenterNet based 3D Object Detection for Autonomous Driving"
},
"1605.02196": {
"arxivId": "1605.02196",
"title": "All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles"
},
"2008.08136": {
"arxivId": "2008.08136",
"title": "DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR"
},
"2008.01942": {
"arxivId": "2008.01942",
"title": "A feature-supervised generative adversarial network for environmental monitoring during hazy days"
},
"2204.00106": {
"arxivId": "2204.00106",
"title": "A Survey of Robust 3D Object Detection Methods in Point Clouds"
},
"2108.12863": {
"arxivId": "2108.12863",
"title": "MBDF-Net: Multi-Branch Deep Fusion Network for 3D Object Detection"
}
} |