{ "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" } }