task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
16k > Object Detection > 3D Object Detection | waymo cyclist | M3DeTR | https://arxiv.org/abs/2104.11896v3 | APH/L2 | 67.28 |
16k > Object Detection > 3D Object Detection | waymo vehicle | PillarNeXt | https://arxiv.org/abs/2305.04925v1 | APH/L2 | 75.76 |
16k > Object Detection > 3D Object Detection | waymo vehicle | DSVT(val) | https://arxiv.org/abs/2301.06051v2 | APH/L2 | 74.1 |
16k > Object Detection > 3D Object Detection | waymo vehicle | DSVT(val) | https://arxiv.org/abs/2301.06051v2 | L1 mAP | 82.1 |
16k > Object Detection > 3D Object Detection | waymo vehicle | CenterFormer | https://arxiv.org/abs/2209.05588v1 | APH/L2 | 73.8 |
16k > Object Detection > 3D Object Detection | waymo vehicle | PV-RCNN | https://arxiv.org/abs/1912.13192v2 | APH/L2 | 73.23 |
16k > Object Detection > 3D Object Detection | waymo vehicle | SST | https://arxiv.org/abs/2112.06375v1 | APH/L2 | 72.74 |
16k > Object Detection > 3D Object Detection | waymo vehicle | M3DeTR | https://arxiv.org/abs/2104.11896v3 | APH/L2 | 70.54 |
16k > Object Detection > 3D Object Detection | waymo vehicle | M3DeTR | https://arxiv.org/abs/2104.11896v3 | L1 mAP | 77.66 |
16k > Object Detection > 3D Object Detection | waymo vehicle | M3DeTR | https://arxiv.org/abs/2104.11896v3 | AP | 77.09 |
16k > Object Detection > 3D Object Detection | waymo vehicle | Pyramid-PV | https://arxiv.org/abs/2109.02499v1 | AP | 76.3 |
16k > Object Detection > 3D Object Detection | waymo vehicle | VoTr-TSD | https://arxiv.org/abs/2109.02497v2 | L1 mAP | 74.95 |
16k > Object Detection > 3D Object Detection | nuScenes-F | RRPN + R101 - F | https://arxiv.org/abs/1905.00526v2 | AP | 43 |
16k > Object Detection > 3D Object Detection | nuScenes-F | RRPN + R101 - F | https://arxiv.org/abs/1905.00526v2 | AP50 | 64.9 |
16k > Object Detection > 3D Object Detection | nuScenes-F | RRPN + R101 - F | https://arxiv.org/abs/1905.00526v2 | AP75 | 48.5 |
16k > Object Detection > 3D Object Detection | nuScenes-F | RRPN + R101 - F | https://arxiv.org/abs/1905.00526v2 | AR | 48.6 |
16k > Object Detection > 3D Object Detection | nuScenes-F | RRPN + R101 - F | https://arxiv.org/abs/1905.00526v2 | ARI | 58.2 |
16k > Object Detection > 3D Object Detection | nuScenes-F | RRPN + R101 - F | https://arxiv.org/abs/1905.00526v2 | ARm | 41.2 |
16k > Object Detection > 3D Object Detection | nuScenes-F | RRPN + R101 - F | https://arxiv.org/abs/1905.00526v2 | ARs | 4 |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Easy | 3D-FCT | https://arxiv.org/abs/2110.02531v1 | AP | 89.15% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Easy | M3DeTR | https://arxiv.org/abs/2104.11896v3 | AP | 83.83% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Easy | F-ConvNet | https://arxiv.org/abs/1903.01864v2 | AP | 79.58% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Easy | SVGA-Net | https://arxiv.org/abs/2006.04043v2 | AP | 79.22% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Easy | STD | https://arxiv.org/abs/1907.10471v1 | AP | 78.89% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Easy | PV-RCNN | https://arxiv.org/abs/1912.13192v2 | AP | 78.60% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Easy | PointPillars | https://arxiv.org/abs/1812.05784v2 | AP | 75.78% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Easy | PointRCNN | https://arxiv.org/abs/1812.04244v2 | AP | 73.93% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Easy | Frustum PointNets | http://arxiv.org/abs/1711.08488v2 | AP | 71.96% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Easy | IPOD | http://arxiv.org/abs/1812.05276v1 | AP | 71.40% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Easy | AVOD + Feature Pyramid | http://arxiv.org/abs/1712.02294v4 | AP | 64.0% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Easy | VoxelNet | http://arxiv.org/abs/1711.06396v1 | AP | 61.22% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrian | PiFeNet | https://arxiv.org/abs/2112.15458v3 | mAP | 0.486 |
16k > Object Detection > 3D Object Detection | Cityscapes 3D | TaskPrompter | https://arxiv.org/abs/2304.00971v3 | mDS | 32.94 |
16k > Object Detection > 3D Object Detection | nuScenes-FB | RRPN + R101 - FB | https://arxiv.org/abs/1905.00526v2 | AP | 35.5 |
16k > Object Detection > 3D Object Detection | nuScenes-FB | RRPN + R101 - FB | https://arxiv.org/abs/1905.00526v2 | AP50 | 59 |
16k > Object Detection > 3D Object Detection | nuScenes-FB | RRPN + R101 - FB | https://arxiv.org/abs/1905.00526v2 | AP75 | 37 |
16k > Object Detection > 3D Object Detection | nuScenes-FB | RRPN + R101 - FB | https://arxiv.org/abs/1905.00526v2 | AR | 42.1 |
16k > Object Detection > 3D Object Detection | nuScenes-FB | RRPN + R101 - FB | https://arxiv.org/abs/1905.00526v2 | ARI | 51.4 |
16k > Object Detection > 3D Object Detection | nuScenes-FB | RRPN + R101 - FB | https://arxiv.org/abs/1905.00526v2 | ARm | 39.1 |
16k > Object Detection > 3D Object Detection | nuScenes-FB | RRPN + R101 - FB | https://arxiv.org/abs/1905.00526v2 | ARs | 21.1 |
16k > Object Detection > 3D Object Detection | waymo pedestrian | DSVT(val) | https://arxiv.org/abs/2301.06051v2 | APH/L2 | 76.4 |
16k > Object Detection > 3D Object Detection | waymo pedestrian | PillarNeXt | https://arxiv.org/abs/2305.04925v1 | APH/L2 | 75.98 |
16k > Object Detection > 3D Object Detection | waymo pedestrian | CenterFormer | https://arxiv.org/abs/2209.05588v1 | APH/L2 | 75.0 |
16k > Object Detection > 3D Object Detection | waymo pedestrian | SST | https://arxiv.org/abs/2112.06375v1 | APH/L2 | 73.51 |
16k > Object Detection > 3D Object Detection | waymo pedestrian | CenterPoint | https://arxiv.org/abs/2006.11275v2 | APH/L2 | 71.52 |
16k > Object Detection > 3D Object Detection | waymo pedestrian | PV-RCNN | https://arxiv.org/abs/1912.13192v2 | APH/L2 | 70.16 |
16k > Object Detection > 3D Object Detection | waymo pedestrian | M3DeTR | https://arxiv.org/abs/2104.11896v3 | APH/L2 | 68.20 |
16k > Object Detection > 3D Object Detection | Heavy Snowfall | PV-RCNN | https://arxiv.org/abs/2203.15118v2 | mod. Car AP@.7IoU | 41.79 |
16k > Object Detection > 3D Object Detection | Dense Fog | PV-RCNN | https://arxiv.org/abs/2108.05249v3 | mod. Car AP@.5IoU | 47.38 |
16k > Object Detection > 3D Object Detection | Dense Fog | PV-RCNN | https://arxiv.org/abs/2108.05249v3 | mod. Cyclist AP@.25IoU | 27.89 |
16k > Object Detection > 3D Object Detection | Dense Fog | PV-RCNN | https://arxiv.org/abs/2108.05249v3 | mod. Pedestrian AP@.25IoU | 40.65 |
16k > Object Detection > 3D Object Detection | Dense Fog | PV-RCNN | https://arxiv.org/abs/2108.05249v3 | mod. mAP | 38.64 |
16k > Object Detection > 3D Object Detection | Aria Synthetic Environments | EVL | https://arxiv.org/abs/2406.10224v1 | MAP | 75 |
16k > Object Detection > 3D Object Detection | Aria Synthetic Environments | ImVoxelNet | https://arxiv.org/abs/2406.10224v1 | MAP | 64 |
16k > Object Detection > 3D Object Detection | Aria Synthetic Environments | Cube R-CNN | https://arxiv.org/abs/2406.10224v1 | MAP | 36 |
16k > Object Detection > 3D Object Detection | Aria Synthetic Environments | 3DETR | https://arxiv.org/abs/2406.10224v1 | MAP | 33 |
16k > Object Detection > 3D Object Detection | TruckScenes | SpaRC | https://arxiv.org/abs/2411.19860v1 | NDS | 37.4 |
16k > Object Detection > 3D Object Detection | TruckScenes | SpaRC | https://arxiv.org/abs/2411.19860v1 | mAP | 27.2 |
16k > Object Detection > 3D Object Detection | TruckScenes | HyDRa | https://arxiv.org/abs/2403.07746v4 | NDS | 22.4 |
16k > Object Detection > 3D Object Detection | TruckScenes | HyDRa | https://arxiv.org/abs/2403.07746v4 | mAP | 12.8 |
16k > Object Detection > 3D Object Detection | TruckScenes | PETR | https://arxiv.org/abs/2203.05625v3 | NDS | 12.1 |
16k > Object Detection > 3D Object Detection | TruckScenes | PETR | https://arxiv.org/abs/2203.05625v3 | mAP | 2.2 |
16k > Object Detection > 3D Object Detection | TruckScenes | RadarGNN | https://arxiv.org/abs/2304.06547v1 | NDS | 10.8 |
16k > Object Detection > 3D Object Detection | TruckScenes | RadarGNN | https://arxiv.org/abs/2304.06547v1 | mAP | 7.0 |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | 3D-FCT | https://arxiv.org/abs/2110.02531v1 | AP | 75.86% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | M3DeTR | https://arxiv.org/abs/2104.11896v3 | AP | 66.74% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | SVGA-Net | https://arxiv.org/abs/2006.04043v2 | AP | 66.13% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | F-ConvNet | https://arxiv.org/abs/1903.01864v2 | AP | 64.68% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | PV-RCNN | https://arxiv.org/abs/1912.13192v2 | AP | 63.71% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | STD | https://arxiv.org/abs/1907.10471v1 | AP | 62.53% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | PointRCNN | https://arxiv.org/abs/1812.04244v2 | AP | 59.60% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | PointPillars | https://arxiv.org/abs/1812.05784v2 | AP | 59.07% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | VoxelNet With Eloss | https://arxiv.org/abs/2302.00986v1 | AP | 58% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | Frustum PointNets | http://arxiv.org/abs/1711.08488v2 | AP | 56.77% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | IPOD | http://arxiv.org/abs/1812.05276v1 | AP | 53.46% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | AVOD + Feature Pyramid | http://arxiv.org/abs/1712.02294v4 | AP | 52.18% |
16k > Object Detection > 3D Object Detection | KITTI Cyclists Moderate | VoxelNet | http://arxiv.org/abs/1711.06396v1 | AP | 48.36% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrians Moderate | 3D-FCT | https://arxiv.org/abs/2110.02531v1 | AP | 58.4% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrians Moderate | SVGA-Net | https://arxiv.org/abs/2006.04043v2 | AP | 47.71% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrians Moderate | HotSpotNet | https://arxiv.org/abs/1912.12791v3 | AP | 44.81% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrians Moderate | IPOD | http://arxiv.org/abs/1812.05276v1 | AP | 44.68% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrians Moderate | STD | https://arxiv.org/abs/1907.10471v1 | AP | 44.24% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrians Moderate | F-ConvNet | https://arxiv.org/abs/1903.01864v2 | AP | 43.38% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrians Moderate | Frustrum-PointPillars | https://ieeexplore.ieee.org/document/9607424 | AP | 42.89 % |
16k > Object Detection > 3D Object Detection | KITTI Pedestrians Moderate | AVOD + Feature Pyramid | http://arxiv.org/abs/1712.02294v4 | AP | 42.81% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrians Moderate | Frustum PointNets | http://arxiv.org/abs/1711.08488v2 | AP | 42.15% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrians Moderate | PointPillars | https://arxiv.org/abs/1812.05784v2 | AP | 41.92% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrians Moderate | M3DeTR | https://arxiv.org/abs/2104.11896v3 | AP | 41.02% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrians Moderate | VoxelNet | http://arxiv.org/abs/1711.06396v1 | AP | 33.69% |
16k > Object Detection > 3D Object Detection | KITTI Pedestrian Easy | PiFeNet | https://arxiv.org/abs/2112.15458v3 | Average Precision | 0.5639 |
16k > Object Detection > 3D Object Detection > Monocular 3D Object Detection | KITTI Cars Easy | CIE | https://arxiv.org/abs/2207.07933v2 | AP Easy | 31.55 |
16k > Object Detection > 3D Object Detection > Monocular 3D Object Detection | KITTI Cars Easy | CMKD | https://arxiv.org/abs/2211.07171v1 | AP Easy | 28.55 |
16k > Object Detection > 3D Object Detection > Monocular 3D Object Detection | KITTI Cars Easy | MonoDGP | https://arxiv.org/abs/2410.19590v1 | AP Easy | 26.35 |
16k > Object Detection > 3D Object Detection > Monocular 3D Object Detection | KITTI Cars Easy | DD3D | https://arxiv.org/abs/2108.06417v1 | AP Easy | 23.22 |
16k > Object Detection > 3D Object Detection > Monocular 3D Object Detection | KITTI Cars Easy | CaDDN | https://arxiv.org/abs/2103.01100v2 | AP Easy | 19.17 |
16k > Object Detection > 3D Object Detection > Monocular 3D Object Detection | KITTI Cyclist Moderate | CMKD | https://arxiv.org/abs/2211.07171v1 | AP Medium | 6.67 |
16k > Object Detection > 3D Object Detection > Monocular 3D Object Detection | KITTI Cyclist Moderate | CaDDN | https://arxiv.org/abs/2103.01100v2 | AP Medium | 3.41 |
16k > Object Detection > 3D Object Detection > Monocular 3D Object Detection | KITTI Cyclist Hard | CMKD | https://arxiv.org/abs/2211.07171v1 | AP Hard | 6.34 |
16k > Object Detection > 3D Object Detection > Monocular 3D Object Detection | KITTI Cyclist Hard | CaDDN | https://arxiv.org/abs/2103.01100v2 | AP Hard | 3.30 |
16k > Object Detection > 3D Object Detection > Monocular 3D Object Detection | KITTI Cars Moderate | CIE | https://arxiv.org/abs/2207.07933v2 | AP Medium | 20.95 |
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