mlsanityData to 14 Oct 2026

Vehicle Detection from 3D Lidar Using Fully Convolutional Network

arXiv 1608.07916 ↗First posted 29 Aug 2016cs.CV cs.ROcited by 107

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Abstract (arXiv)

Convolutional network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully convolutional network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end-to-end fully convolutional network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D convolutional network. Experiments on the KITTI dataset shows the state-of-the-art performance of the proposed method.

Key facts. Cited by 107 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Aug 2016.

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Citing papers per year: 2021: 28; 2022: 32; 2023: 18; 2024: 19; 2025: 6; 2026: 4.

107 papers.

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  1. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
  2. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
  3. 2022Unifying Voxel-based Representation with Transformer for 3D Object Detectioncited by 202
  4. 2022FUTR3D: A Unified Sensor Fusion Framework for 3D Detectioncited by 194
  5. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  6. 2021IntentNet: Learning to Predict Intention from Raw Sensor Datacited by 158
  7. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 145
  8. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
  9. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  10. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
  11. 2021Improving 3D Object Detection with Channel-wise Transformercited by 108
  12. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
38 more
  1. 2022Learning to Generate Realistic LiDAR Point Cloudscited by 93
  2. 2023Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Trainingcited by 86
  3. 2024Towards Realistic Scene Generation with LiDAR Diffusion Modelscited by 69
  4. 2023Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Reviewcited by 58
  5. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  6. 2022Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Imagescited by 56
  7. 2023Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learningcited by 56
  8. 2022LidarGait: Benchmarking 3D Gait Recognition with Point Cloudscited by 55
  9. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  10. 2024RangeLDM: Fast Realistic LiDAR Point Cloud Generationcited by 47
  11. 20214D-Net for Learned Multi-Modal Alignmentcited by 44
  12. 2024OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentationcited by 44
  13. 2021Auto4D: Learning to Label 4D Objects from Sequential Point Cloudscited by 42
  14. 2021To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernelscited by 39
  15. 2022DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detectioncited by 39
  16. 2023MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representationscited by 33
  17. 2023AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Drivingcited by 32
  18. 2022PointConvFormer: Revenge of the Point-based Convolutioncited by 30
  19. 2021Learning to Drop Points for LiDAR Scan Synthesiscited by 27
  20. 2022OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Cloudscited by 25
  21. 2024SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detectioncited by 25
  22. 2022A Closer Look at Invariances in Self-supervised Pre-training for 3D Visioncited by 24
  23. 2024CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformerscited by 21
  24. 2021MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Datacited by 18
  25. 2022WeakM3D: Towards Weakly Supervised Monocular 3D Object Detectioncited by 18
  26. 2021Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detectioncited by 16
  27. 2022LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentationcited by 16
  28. 2022INT: Towards Infinite-frames 3D Detection with An Efficient Frameworkcited by 14
  29. 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
  30. 2024X-Drive: Cross-modality consistent multi-sensor data synthesis for driving scenarioscited by 14
  31. 2023MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequencescited by 13
  32. 2021SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimationcited by 12
  33. 2022HFT: Lifting Perspective Representations via Hybrid Feature Transformationcited by 11
  34. 2021S3Net: 3D LiDAR Sparse Semantic Segmentation Networkcited by 9
  35. 2022Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancementcited by 9
  36. 2024OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Imagescited by 9
  37. 2021Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaborationcited by 8
  38. 2021Local Aggressive Adversarial Attacks on 3D Point Cloudcited by 7

50 most cited of 107 All 107 (JSON)