SurveyBench / human_written_ref /3D Object Detection for Autonomous Driving: A Comprehensive Survey.json
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| "title": "3D Object Detection From Images for Autonomous Driving: A Survey" | |
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| "title": "From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection" | |
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| "title": "Wasserstein Distances for Stereo Disparity Estimation" | |
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| "title": "To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels" | |
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| "title": "BirdNet+: End-to-End 3D Object Detection in LiDAR Bird\u2019s Eye View" | |
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| "title": "Learning to Evaluate Perception Models Using Planner-Centric Metrics" | |
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| "title": "Voxel Field Fusion for 3D Object Detection" | |
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| "title": "ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection" | |
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| "title": "Relation Graph Network for 3D Object Detection in Point Clouds" | |
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| "title": "YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection" | |
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| "title": "Towards Generalization Across Depth for Monocular 3D Object Detection" | |
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| "title": "SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations" | |
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| "title": "STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction" | |
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| "2006.07864": { | |
| "arxivId": "2006.07864", | |
| "title": "Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection" | |
| }, | |
| "2208.10145": { | |
| "arxivId": "2208.10145", | |
| "title": "STS: Surround-view Temporal Stereo for Multi-view 3D Detection" | |
| }, | |
| "2007.13970": { | |
| "arxivId": "2007.13970", | |
| "title": "Weakly Supervised 3D Object Detection from Point Clouds" | |
| }, | |
| "2002.05316": { | |
| "arxivId": "2002.05316", | |
| "title": "SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud" | |
| }, | |
| "1911.12236": { | |
| "arxivId": "1911.12236", | |
| "title": "PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement" | |
| }, | |
| "2204.00325": { | |
| "arxivId": "2204.00325", | |
| "title": "CAT-Det: Contrastively Augmented Transformer for Multimodal 3D Object Detection" | |
| }, | |
| "1909.07701": { | |
| "arxivId": "1909.07701", | |
| "title": "Task-Aware Monocular Depth Estimation for 3D Object Detection" | |
| }, | |
| "2011.05289": { | |
| "arxivId": "2011.05289", | |
| "title": "Learning to Communicate and Correct Pose Errors" | |
| }, | |
| "2112.14023": { | |
| "arxivId": "2112.14023", | |
| "title": "The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection" | |
| }, | |
| "2109.01066": { | |
| "arxivId": "2109.01066", | |
| "title": "4D-Net for Learned Multi-Modal Alignment" | |
| }, | |
| "1809.06065": { | |
| "arxivId": "1809.06065", | |
| "title": "Focal Loss in 3D Object Detection" | |
| }, | |
| "2108.03648": { | |
| "arxivId": "2108.03648", | |
| "title": "From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder" | |
| }, | |
| "2004.01170": { | |
| "arxivId": "2004.01170", | |
| "title": "DOPS: Learning to Detect 3D Objects and Predict Their 3D Shapes" | |
| }, | |
| "2101.06560": { | |
| "arxivId": "2101.06560", | |
| "title": "Adversarial Attacks On Multi-Agent Communication" | |
| }, | |
| "2101.06586": { | |
| "arxivId": "2101.06586", | |
| "title": "Auto4D: Learning to Label 4D Objects from Sequential Point Clouds" | |
| }, | |
| "2108.07142": { | |
| "arxivId": "2108.07142", | |
| "title": "PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation" | |
| }, | |
| "2103.15326": { | |
| "arxivId": "2103.15326", | |
| "title": "Fooling LiDAR Perception via Adversarial Trajectory Perturbation" | |
| }, | |
| "2003.05505": { | |
| "arxivId": "2003.05505", | |
| "title": "Confidence Guided Stereo 3D Object Detection with Split Depth Estimation" | |
| }, | |
| "2011.01153": { | |
| "arxivId": "2011.01153", | |
| "title": "Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving" | |
| }, | |
| "2103.02093": { | |
| "arxivId": "2103.02093", | |
| "title": "Pseudo-labeling for Scalable 3D Object Detection" | |
| }, | |
| "2009.14524": { | |
| "arxivId": "2009.14524", | |
| "title": "Monocular Differentiable Rendering for Self-Supervised 3D Object Detection" | |
| }, | |
| "2010.08243": { | |
| "arxivId": "2010.08243", | |
| "title": "SF-UDA3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection" | |
| }, | |
| "2003.05982": { | |
| "arxivId": "2003.05982", | |
| "title": "LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting" | |
| }, | |
| "2101.06720": { | |
| "arxivId": "2101.06720", | |
| "title": "Deep Multi-Task Learning for Joint Localization, Perception, and Prediction" | |
| }, | |
| "2105.07647": { | |
| "arxivId": "2105.07647", | |
| "title": "FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection" | |
| }, | |
| "2112.01135": { | |
| "arxivId": "2112.01135", | |
| "title": "Open-set 3D Object Detection" | |
| }, | |
| "2101.06594": { | |
| "arxivId": "2101.06594", | |
| "title": "PLUMENet: Efficient 3D Object Detection from Stereo Images" | |
| }, | |
| "2008.10436": { | |
| "arxivId": "2008.10436", | |
| "title": "Cross-Modality 3D Object Detection" | |
| }, | |
| "2208.11658": { | |
| "arxivId": "2208.11658", | |
| "title": "AGO-Net: Association-Guided 3D Point Cloud Object Detection Network" | |
| }, | |
| "2108.03634": { | |
| "arxivId": "2108.03634", | |
| "title": "Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud" | |
| }, | |
| "2004.02724": { | |
| "arxivId": "2004.02724", | |
| "title": "Reconfigurable Voxels: A New Representation for LiDAR-Based Point Clouds" | |
| }, | |
| "2005.01864": { | |
| "arxivId": "2005.01864", | |
| "title": "Streaming Object Detection for 3-D Point Clouds" | |
| }, | |
| "2103.14198": { | |
| "arxivId": "2103.14198", | |
| "title": "Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection in Self-Driving Cars" | |
| }, | |
| "2112.07787": { | |
| "arxivId": "2112.07787", | |
| "title": "Revisiting 3D Object Detection From an Egocentric Perspective" | |
| }, | |
| "2012.02938": { | |
| "arxivId": "2012.02938", | |
| "title": "Cirrus: A Long-range Bi-pattern LiDAR Dataset" | |
| }, | |
| "2110.00464": { | |
| "arxivId": "2110.00464", | |
| "title": "MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation" | |
| }, | |
| "2106.07545": { | |
| "arxivId": "2106.07545", | |
| "title": "PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars" | |
| }, | |
| "2108.09663": { | |
| "arxivId": "2108.09663", | |
| "title": "SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation" | |
| }, | |
| "2102.11952": { | |
| "arxivId": "2102.11952", | |
| "title": "Learning to Drop Points for LiDAR Scan Synthesis" | |
| }, | |
| "2012.03121": { | |
| "arxivId": "2012.03121", | |
| "title": "It\u2019s All Around You: Range-Guided Cylindrical Network for 3D Object Detection" | |
| }, | |
| "2103.05929": { | |
| "arxivId": "2103.05929", | |
| "title": "MapFusion: A General Framework for 3D Object Detection with HDMaps" | |
| }, | |
| "2301.07870": { | |
| "arxivId": "2301.07870", | |
| "title": "Fast-BEV: Towards Real-time On-vehicle Bird's-Eye View Perception" | |
| }, | |
| "2212.02181": { | |
| "arxivId": "2212.02181", | |
| "title": "Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction" | |
| }, | |
| "2008.06020": { | |
| "arxivId": "2008.06020", | |
| "title": "Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction" | |
| }, | |
| "2203.08332": { | |
| "arxivId": "2203.08332", | |
| "title": "WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection" | |
| }, | |
| "2011.06425": { | |
| "arxivId": "2011.06425", | |
| "title": "StrObe: Streaming Object Detection from LiDAR Packets" | |
| }, | |
| "2005.10863": { | |
| "arxivId": "2005.10863", | |
| "title": "RV-FuseNet: Range View Based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting" | |
| }, | |
| "2203.13394": { | |
| "arxivId": "2203.13394", | |
| "title": "Point2Seq: Detecting 3D Objects as Sequences" | |
| }, | |
| "2006.16007": { | |
| "arxivId": "2006.16007", | |
| "title": "MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time" | |
| }, | |
| "2110.09355": { | |
| "arxivId": "2110.09355", | |
| "title": "FAST3D: Flow-Aware Self-Training for 3D Object Detectors" | |
| } | |
| } |