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LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving

arXiv 1903.08701 ↗First posted 20 Mar 2019cs.CV cs.LG cs.ROcited by 133

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Abstract (arXiv)

In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact. Operating in the range view involves well known challenges for learning, including occlusion and scale variation, but it also provides contextual information based on how the sensor data was captured. Our approach uses a fully convolutional network to predict a multimodal distribution over 3D boxes for each point and then it efficiently fuses these distributions to generate a prediction for each object. Experiments show that modeling each detection as a distribution rather than a single deterministic box leads to better overall detection performance. Benchmark results show that this approach has significantly lower runtime than other recent detectors and that it achieves state-of-the-art performance when compared on a large dataset that has enough data to overcome the challenges of training on the range view.

Key facts. Cited by 133 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Mar 2019.

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Citing papers per year: 2020: 1; 2021: 29; 2022: 36; 2023: 32; 2024: 21; 2025: 7; 2026: 7.

133 papers.

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  1. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
  2. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 372
  3. 2021Voxel Transformer for 3D Object Detectioncited by 247
  4. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  5. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  6. 2021MP3: A Unified Model to Map, Perceive, Predict and Plancited by 211
  7. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  8. 2021Geometry Uncertainty Projection Network for Monocular 3D Object Detectioncited by 155
  9. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 144
  10. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
  11. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  12. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
38 more
  1. 2021RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detectioncited by 115
  2. 2023Visual Point Cloud Forecasting enables Scalable Autonomous Drivingcited by 97
  3. 2021SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generationcited by 93
  4. 2022SWFormer: Sparse Window Transformer for 3D Object Detection in Point Cloudscited by 92
  5. 2021AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Cloudscited by 88
  6. 2021Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detectioncited by 79
  7. 2021M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformerscited by 74
  8. 2024Towards Realistic Scene Generation with LiDAR Diffusion Modelscited by 67
  9. 2022Motion Inspired Unsupervised Perception and Prediction in Autonomous Drivingcited by 59
  10. 2021Object DGCNN: 3D Object Detection using Dynamic Graphscited by 58
  11. 2022Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Imagescited by 56
  12. 2023PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Cloudscited by 54
  13. 2022Polar Parametrization for Vision-based Surround-View 3D Detectioncited by 53
  14. 2021EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detectioncited by 49
  15. 2022VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attentioncited by 48
  16. 2022Uncertainty Quantification of Collaborative Detection for Self-Drivingcited by 46
  17. 2024RangeLDM: Fast Realistic LiDAR Point Cloud Generationcited by 45
  18. 20214D-Net for Learned Multi-Modal Alignmentcited by 44
  19. 2021Auto4D: Learning to Label 4D Objects from Sequential Point Cloudscited by 42
  20. 2021To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernelscited by 39
  21. 2023CAPE: Camera View Position Embedding for Multi-View 3D Object Detectioncited by 39
  22. 2021Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectorscited by 37
  23. 2023Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Drivingcited by 37
  24. 2022Towards Efficient 3D Object Detection with Knowledge Distillationcited by 29
  25. 2024SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decompositioncited by 29
  26. 2023Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detectioncited by 28
  27. 2023Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labelingcited by 28
  28. 2022LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detectioncited by 27
  29. 2023Collaborative Multi-Object Tracking with Conformal Uncertainty Propagationcited by 27
  30. 2021LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignmentcited by 26
  31. 2021Uncertainty for Identifying Open-Set Errors in Visual Object Detectioncited by 26
  32. 2024Text2LiDAR: Text-guided LiDAR Point Cloud Generation via Equirectangular Transformercited by 26
  33. 2024SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detectioncited by 25
  34. 2021From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detectioncited by 24
  35. 2022GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimationcited by 24
  36. 2024LISO: Lidar-only Self-Supervised 3D Object Detectioncited by 22
  37. 2023PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformercited by 21
  38. 2022Improving the Intra-class Long-tail in 3D Detection via Rare Example Miningcited by 19

50 most cited of 133 All 133 (JSON)