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| "title": "Multi-modal sensor fusion for auto driving perception: A survey" | |
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| "title": "Multi-Modal 3D Object Detection in Autonomous Driving: A Survey" | |
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| "title": "Voxel Field Fusion for 3D Object Detection" | |
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| "title": "3D Object Detection for Autonomous Driving: A Comprehensive Survey" | |
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| "title": "RoIFusion: 3D Object Detection From LiDAR and Vision" | |
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| "title": "Bridged Transformer for Vision and Point Cloud 3D Object Detection" | |
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| "title": "Monocular 3D Object Detection With Sequential Feature Association and Depth Hint Augmentation" | |
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| "title": "MLOD: A multi-view 3D object detection based on robust feature fusion method" | |
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| "title": "Cross-Modality 3D Object Detection" | |
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| "title": "Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion" | |
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| "title": "PatchNet: Hierarchical Deep Learning-Based Stable Patch Identification for the Linux Kernel" | |
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| "title": "MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion" | |
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| "title": "Improving 3D Object Detection for Pedestrians with Virtual Multi-View Synthesis Orientation Estimation" | |
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| "title": "Cirrus: A Long-range Bi-pattern LiDAR Dataset" | |
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| "title": "SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation" | |
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| "title": "Multi-View Adaptive Fusion Network for 3D Object Detection" | |
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| "title": "4D Unsupervised Object Discovery" | |
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| "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" | |
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| "title": "Microsoft COCO: Common Objects in Context" | |
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| "arxivId": "1506.02640", | |
| "title": "You Only Look Once: Unified, Real-Time Object Detection" | |
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| "title": "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation" | |
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| "title": "YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors" | |
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| "title": "EfficientDet: Scalable and Efficient Object Detection" | |
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| "arxivId": "1807.05511", | |
| "title": "Object Detection With Deep Learning: A Review" | |
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| "title": "Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving" | |
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| "title": "A Survey of Deep Learning-Based Object Detection" | |
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| "title": "Oriented R-CNN for Object Detection" | |
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| "title": "Stereo R-CNN Based 3D Object Detection for Autonomous Driving" | |
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| "title": "BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection" | |
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| "title": "M3D-RPN: Monocular 3D Region Proposal Network for Object Detection" | |
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| "arxivId": "1905.12365", | |
| "title": "Disentangling monocular 3d object detection" | |
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| "title": "DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems" | |
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| "title": "Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming" | |
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| "title": "Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving" | |
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| "title": "3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection" | |
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| "title": "SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation" | |
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| "title": "Is Pseudo-Lidar needed for Monocular 3D Object detection?" | |
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| "title": "Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving" | |
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| "title": "Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud" | |
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| "title": "Canadian Adverse Driving Conditions dataset" | |
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| "title": "Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques" | |
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| "title": "Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving" | |
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| "title": "Real-Time Dense Mapping for Self-Driving Vehicles using Fisheye Cameras" | |
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| "title": "Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving" | |
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| "title": "3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection" | |
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| "title": "Dynamic Graph CNN for Learning on Point Clouds" | |
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| "title": "Object Detection in 20 Years: A Survey" | |
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| "title": "PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection" | |
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| "title": "PIXOR: Real-time 3D Object Detection from Point Clouds" | |
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| "title": "3DSSD: Point-Based 3D Single Stage Object Detector" | |
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| "title": "From Points to Parts: 3D Object Detection From Point Cloud With Part-Aware and Part-Aggregation Network" | |
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| "title": "What Makes for Effective Detection Proposals?" | |
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| "title": "Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection" | |
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| "title": "Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images" | |
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| "title": "Point-Voxel CNN for Efficient 3D Deep Learning" | |
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| "title": "Vehicle Detection from 3D Lidar Using Fully Convolutional Network" | |
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| "title": "Multi-Task Multi-Sensor Fusion for 3D Object Detection" | |
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| "title": "Voxel Transformer for 3D Object Detection" | |
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| "title": "Fast Point R-CNN" | |
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| "arxivId": "1908.03851", | |
| "title": "IoU Loss for 2D/3D Object Detection" | |
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| "arxivId": "1910.06528", | |
| "title": "End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds" | |
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| "title": "TANet: Robust 3D Object Detection from Point Clouds with Triple Attention" | |
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| "title": "SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud" | |
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| "arxivId": "2012.03015", | |
| "title": "CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud" | |
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| "title": "Objects are Different: Flexible Monocular 3D Object Detection" | |
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| "arxivId": "2004.00543", | |
| "title": "Physically Realizable Adversarial Examples for LiDAR Object Detection" | |
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| "title": "Improving 3D Object Detection with Channel-wise Transformer" | |
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| "arxivId": "2103.16237", | |
| "title": "Delving into localization errors for monocular 3D object detection" | |
| }, | |
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| "arxivId": "2003.00186", | |
| "title": "HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection" | |
| }, | |
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| "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" | |
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| "arxivId": "1804.05178", | |
| "title": "LiDAR and Camera Calibration Using Motions Estimated by Sensor Fusion Odometry" | |
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| "arxivId": "1912.00202", | |
| "title": "Relation Graph Network for 3D Object Detection in Point Clouds" | |
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| "title": "PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement" | |
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| "title": "BADET: Boundary-aware 3d object detection from point clouds" | |
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| "title": "Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds" | |
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| "arxivId": "1906.05113", | |
| "title": "A survey of autonomous driving: Common practices and emerging technologies" | |
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| "arxivId": "2002.00444", | |
| "title": "Deep reinforcement learning for autonomous driving: A survey" | |
| }, | |
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| "arxivId": "2202.02980", | |
| "title": "3D Object Detection From Images for Autonomous Driving: A Survey" | |
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| "arxivId": "2312.03031", | |
| "title": "Is ego status all you need for open-loop end-to-end autonomous driving?" | |
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| "arxivId": "2306.16927", | |
| "title": "End-to-end autonomous driving: Challenges and frontiers" | |
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| "title": "CARLA: An Open Urban Driving Simulator" | |
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| "arxivId": "2005.03778", | |
| "title": "LGSVL simulator: A high fidelity simulator for autonomous driving" | |
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| "title": "AirSim: High-fidelity visual and physical simulation for autonomous vehicles" | |
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| "arxivId": "2304.14365", | |
| "title": "OCC3D: A large-scale 3D occupancy prediction benchmark for autonomous driving" | |
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| "arxivId": "2109.07644", | |
| "title": "OPV2V: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication" | |
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| "arxivId": "2202.08449", | |
| "title": "V2X-Sim: Multi-agent collaborative perception dataset and benchmark for autonomous driving" | |
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| "arxivId": "2403.01316", | |
| "title": "TUMTraf V2X cooperative perception dataset" | |
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| "title": "YOLOv3: An incremental improvement" | |
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| "arxivId": "2104.10956", | |
| "title": "FCOS3D: Fully convolutional one-stage monocular 3d object detection" | |
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| "arxivId": "1904.08506", | |
| "title": "Adaptive hierarchical down-sampling for point cloud classification" | |
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| "title": "Point2Seq: Detecting 3d objects as sequences" | |
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| "arxivId": "2303.11301", | |
| "title": "VoxelNext: Fully sparse voxelnet for 3d object detection and tracking" | |
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| "arxivId": "2403.15241", | |
| "title": "IS-Fusion: Instance-scene collaborative fusion for multimodal 3d object detection" | |
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| "title": "Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net" | |
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| "title": "An LSTM approach to temporal 3d object detection in lidar point clouds" | |
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| "title": "Lidar-based online 3d video object detection with graph-based message passing and spatiotemporal transformer attention" | |
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| "title": "STINet: Spatio-temporal-interactive network for pedestrian detection and trajectory prediction" | |
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| "title": "Temporal-channel transformer for 3d lidar-based video object detection for autonomous driving" | |
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| "arxivId": "1811.10742", | |
| "title": "Joint monocular 3d vehicle detection and tracking" | |
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| "arxivId": "1803.01271", | |
| "title": "An empirical evaluation of generic convolutional and recurrent networks for sequence modeling" | |
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| "arxivId": "2303.11926", | |
| "title": "Exploring object-centric temporal modeling for efficient multi-view 3d object detection" | |
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| "title": "Segmenting the future" | |
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| "title": "Single level feature-to-feature forecasting with deformable convolutions" | |
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| "title": "Vehicle-infrastructure cooperative 3d object detection via feature flow prediction" | |
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| "arxivId": "2311.01682", | |
| "title": "Flow-based feature fusion for vehicle-infrastructure cooperative 3d object detection" | |
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| "title": "PointOcc: Cylindrical tri-perspective view for point-based 3d semantic occupancy prediction" | |
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| "title": "Semantic scene completion using local deep implicit functions on lidar data" | |
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| "arxivId": "2310.11239", | |
| "title": "Lidar-based 4d occupancy completion and forecasting" | |
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| "title": "MonoScene: Monocular 3d semantic scene completion" | |
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| "title": "Tri-perspective view for vision-based 3d semantic occupancy prediction" | |
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| "title": "Scene as occupancy" | |
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| "title": "SelfOcc: Self-supervised vision-based 3d occupancy prediction" | |
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| "title": "Cam4DOcc: Benchmark for camera-only 4d occupancy forecasting in autonomous driving applications" | |
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| "title": "OpenOccupancy: A large scale benchmark for surrounding semantic occupancy perception" | |
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| "title": "End to end learning for self-driving cars" | |
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| "title": "Controlling steering angle for cooperative self-driving vehicles utilizing cnn and lstm-based deep networks" | |
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| "title": "A reduction of imitation learning and structured prediction to no-regret online learning" | |
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| "title": "Keyframe-focused visual imitation learning" | |
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| "title": "Object-aware regularization for addressing causal confusion in imitation learning" | |
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| "title": "Proximal policy optimization algorithms" | |
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| "title": "Continuous control with deep reinforcement learning" | |
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| "title": "Perceive, predict, and plan: Safe motion planning through interpretable semantic representations" | |
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| "title": "MP3: A unified model to map, perceive, predict and plan" | |
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| "arxivId": "2212.10156", | |
| "title": "Planning-oriented autonomous driving" | |
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| "arxivId": "2205.15997", | |
| "title": "TransFuser: Imitation with transformer-based sensor fusion for autonomous driving" | |
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| "arxivId": "2402.11502", | |
| "title": "GenAD: Generative end-to-end autonomous driving" | |
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| "arxivId": "2311.12320", | |
| "title": "A survey on multimodal large language models for autonomous driving" | |
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| "title": "HiLM-D: Towards high-resolution understanding in multimodal large language models for autonomous driving" | |
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| "title": "Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving" | |
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| "title": "Drive like a human: Rethinking autonomous driving with large language models" | |
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| "title": "Driving with LLMs: Fusing object-level vector modality for explainable autonomous driving" | |
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| "title": "Embodied understanding of driving scenarios" | |
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| "title": "Collaboration helps camera overtake lidar in 3d detection" | |
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| "title": "CodeFill: Multi-token code completion by jointly learning from structure and naming sequences" | |
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| "title": "Collaborative perception in autonomous driving: Methods, datasets, and challenges" | |
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| "title": "LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross- Modal Fusion" | |
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| "arxivId": "2306.10013", | |
| "title": "PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation" | |
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| "arxivId": "1409.1556", | |
| "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition" | |
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| "arxivId": "1605.06211", | |
| "title": "Fully convolutional networks for semantic segmentation" | |
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| "arxivId": "1608.06993", | |
| "title": "Densely Connected Convolutional Networks" | |
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| "arxivId": "1503.02531", | |
| "title": "Distilling the Knowledge in a Neural Network" | |
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| "title": "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs" | |
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| "title": "Deep learning in neural networks: An overview" | |
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| "arxivId": "1408.5882", | |
| "title": "Convolutional Neural Networks for Sentence Classification" | |
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| "arxivId": "1604.01685", | |
| "title": "The Cityscapes Dataset for Semantic Urban Scene Understanding" | |
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| "arxivId": "1711.07971", | |
| "title": "Non-local Neural Networks" | |
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| "arxivId": "1411.1792", | |
| "title": "How transferable are features in deep neural networks?" | |
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| "arxivId": "1806.09055", | |
| "title": "DARTS: Differentiable Architecture Search" | |
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| "arxivId": "1611.10012", | |
| "title": "Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors" | |
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| "arxivId": "1608.02192", | |
| "title": "Playing for Data: Ground Truth from Computer Games" | |
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| "arxivId": "1802.03601", | |
| "title": "Deep Visual Domain Adaptation: A Survey" | |
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| "arxivId": "1611.05009", | |
| "title": "OctNet: Learning Deep 3D Representations at High Resolutions" | |
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| "arxivId": "1904.09664", | |
| "title": "Deep Hough Voting for 3D Object Detection in Point Clouds" | |
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| "arxivId": "1605.06457", | |
| "title": "VirtualWorlds as Proxy for Multi-object Tracking Analysis" | |
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| "arxivId": "1703.07511", | |
| "title": "Deep Photo Style Transfer" | |
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| "arxivId": "2007.16100", | |
| "title": "Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution" | |
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| "arxivId": "2101.06742", | |
| "title": "Deep Parametric Continuous Convolutional Neural Networks" | |
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| "arxivId": "1611.08069", | |
| "title": "3D fully convolutional network for vehicle detection in point cloud" | |
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| "arxivId": "1807.00652", | |
| "title": "PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation" | |
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| "arxivId": "2012.11409", | |
| "title": "3D Object Detection with Pointformer" | |
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| "arxivId": "1809.07941", | |
| "title": "LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks" | |
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| "arxivId": "2203.17054", | |
| "title": "BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection" | |
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| "arxivId": "1810.10093", | |
| "title": "Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data" | |
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| "arxivId": "1805.01195", | |
| "title": "BirdNet: A 3D Object Detection Framework from LiDAR Information" | |
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| "arxivId": "2011.04841", | |
| "title": "CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection" | |
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| "arxivId": "1904.11621", | |
| "title": "Meta-Sim: Learning to Generate Synthetic Datasets" | |
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| "arxivId": "2205.02833", | |
| "title": "Cross-view Transformers for real-time Map-view Semantic Segmentation" | |
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| "arxivId": "2003.13402", | |
| "title": "Predicting Semantic Map Representations From Images Using Pyramid Occupancy Networks" | |
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| "arxivId": "1811.10247", | |
| "title": "MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization" | |
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| "arxivId": "2006.09348", | |
| "title": "LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World" | |
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| "arxivId": "2103.10039", | |
| "title": "RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection" | |
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| "arxivId": "2012.14176", | |
| "title": "Deep Visual Domain Adaptation" | |
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| "arxivId": "2010.09076", | |
| "title": "RADIATE: A Radar Dataset for Automotive Perception in Bad Weather" | |
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| "arxivId": "1511.03240", | |
| "title": "Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer" | |
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| "arxivId": "1901.10951", | |
| "title": "Distant Vehicle Detection Using Radar and Vision" | |
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| "arxivId": "1707.03167", | |
| "title": "RegNet: Multimodal sensor registration using deep neural networks" | |
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| "arxivId": "2004.00448", | |
| "title": "Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy" | |
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| "arxivId": "1902.03334", | |
| "title": "Photorealistic Image Synthesis for Object Instance Detection" | |
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| "arxivId": "1905.00526", | |
| "title": "RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles" | |
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| "arxivId": "1811.10800", | |
| "title": "Probabilistic Object Detection: Definition and Evaluation" | |
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| "arxivId": "2104.11896", | |
| "title": "M3DETR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers" | |
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| "arxivId": "2007.14366", | |
| "title": "RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects" | |
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| "arxivId": "2105.04619", | |
| "title": "Enhancing Photorealism Enhancement" | |
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| "arxivId": "1901.02237", | |
| "title": "3D Object Detection Using Scale Invariant and Feature Reweighting Networks" | |
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| "arxivId": "1909.07566", | |
| "title": "Object-Centric Stereo Matching for 3D Object Detection" | |
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| "arxivId": "2009.00206", | |
| "title": "RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation" | |
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| "arxivId": "2107.14391", | |
| "title": "From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection" | |
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| "arxivId": "2006.07864", | |
| "title": "Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection" | |
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| "2206.10555": { | |
| "arxivId": "2206.10555", | |
| "title": "Scaling up Kernels in 3D CNNs" | |
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| "arxivId": "2109.00892", | |
| "title": "KITTI-CARLA: a KITTI-like dataset generated by CARLA Simulator" | |
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| "arxivId": "2103.02093", | |
| "title": "Pseudo-labeling for Scalable 3D Object Detection" | |
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| "arxivId": "2103.16694", | |
| "title": "Geometric Unsupervised Domain Adaptation for Semantic Segmentation" | |
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| "arxivId": "2006.15505", | |
| "title": "1st Place Solution for Waymo Open Dataset Challenge - 3D Detection and Domain Adaptation" | |
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| "arxivId": "2012.12741", | |
| "title": "Multi-Modality Cut and Paste for 3D Object Detection" | |
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| "arxivId": "2003.00851", | |
| "title": "Deep Learning on Radar Centric 3D Object Detection" | |
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| "arxivId": "2107.02493", | |
| "title": "Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting" | |
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| "arxivId": "1709.07492", | |
| "title": "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image" | |
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| "arxivId": "1702.05374", | |
| "title": "Domain Adaptation for Visual Applications: A Comprehensive Survey" | |
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| "arxivId": "2301.06051", | |
| "title": "DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets" | |
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| "arxivId": "2212.05867", | |
| "title": "ALSO: Automotive Lidar Self-Supervision by Occupancy Estimation" | |
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| "arxivId": "2301.10222", | |
| "title": "RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving" | |
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| "arxivId": "2201.07706", | |
| "title": "Object Detection in Autonomous Vehicles: Status and Open Challenges" | |
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| "arxivId": "2304.00670", | |
| "title": "CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception" | |
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| "arxivId": "2308.07732", | |
| "title": "UniTR: A Unified and Efficient Multi-Modal Transformer for Bird\u2019s-Eye-View Representation" | |
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| "arxivId": "2010.15614", | |
| "title": "An Overview Of 3D Object Detection" | |
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| "arxivId": "2303.02203", | |
| "title": "X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection" | |
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| "arxivId": "2103.00550", | |
| "title": "A Survey on Deep Semi-Supervised Learning" | |
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| "arxivId": "2006.07529", | |
| "title": "Rethinking the Value of Labels for Improving Class-Imbalanced Learning" | |
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| "arxivId": "2102.00463", | |
| "title": "PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection" | |
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| "arxivId": "2006.14480", | |
| "title": "One Thousand and One Hours: Self-driving Motion Prediction Dataset" | |
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| "arxivId": "1802.00036", | |
| "title": "In Defense of Classical Image Processing: Fast Depth Completion on the CPU" | |
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| "arxivId": "2008.13719", | |
| "title": "RESA: Recurrent Feature-Shift Aggregator for Lane Detection" | |
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| "2106.04538": { | |
| "arxivId": "2106.04538", | |
| "title": "What Makes Multimodal Learning Better than Single (Provably)" | |
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| "arxivId": "2203.11089", | |
| "title": "PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark" | |
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| "arxivId": "1803.00387", | |
| "title": "A General Pipeline for 3D Detection of Vehicles" | |
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| "arxivId": "1904.01206", | |
| "title": "Progressive LiDAR adaptation for road detection" | |
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| "arxivId": "2004.02774", | |
| "title": "SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds" | |
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| "arxivId": "2207.12654", | |
| "title": "ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection" | |
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| "arxivId": "2207.12655", | |
| "title": "Semi-supervised 3D Object Detection with Proficient Teachers" | |
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| "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" | |
| } | |
| } |