每天一篇論文 320/365 LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving 摘要 本文提出了一種基於激光雷達數據的自動駕駛三維目標檢測算法LaserNet。效率來自於在傳感器的原始範圍視圖中處理激光雷達數據，在該視圖中，輸入數據自然緊湊。在範圍視圖中操作涉及到衆所周知的學習挑戰，包括遮擋和尺度變化，但它也提供了基於如
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相關文章
- 1. 每天一篇論文 373/1000 PSEUDO-LIDAR++:ACCURATE DEPTH FOR 3D OBJECT DETECTION IN AUTONOMOUS DRIVING
- 2. 每天一篇論文 324/365RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving
- 3. Multi-View 3D Object Detection Network for Autonomous Driving
- 4. Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 5. 論文閱讀CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 6. [論文解讀]Multi-View 3D Object Detection Network for Autonomous Driving
- 7. Pillar-based Object Detection for Autonomous Driving
- 8. 【3D目標檢測】Multi-View 3D Object Detection Network for Autonomous Driving論文綜述（2016）
- 9. 論文筆記_CV_AD_Visual Perception for Autonomous Driving
- 10. 解讀ECCV2020：Pillar-based Object Detection for Autonomous Driving