【閱讀】-- PillarFlow : End-to-end Birds-eye-view Flow Estimation for Autonomous Driving
IROS 2020 ， Toyota Research Institute 的一篇文章。 本文提出了一種基於激光雷達的場景運動估計器，該估計器與目標檢測分離，因此具有互補性。 我們的方法將兩個連續的完整LIDAR點雲掃描作爲輸入。 每個LIDAR掃描都編碼爲學習的特徵向量的離散化2D BeV表示（也稱爲「Pillars」）。 然後，基於PWC-Net工作改編了光流網絡，以局部匹配兩個連續的Be
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相關文章
- 1. 論文閱讀CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 2. 解讀ECCV2020：Pillar-based Object Detection for Autonomous Driving
- 3. 【論文閱讀】A survey of deep learning techniques for autonomous driving
- 4. Pillar-based Object Detection for Autonomous Driving
- 5. YUVMultiNet Real-time YUV multi-task CNN for autonomous driving
- 6. 《Virtual to Real Reinforcement Learning for Autonomous Driving》翻譯
- 7. 論文筆記_CV_AD_Visual Perception for Autonomous Driving
- 8. Multi-View 3D Object Detection Network for Autonomous Driving
- 9. Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 10. [論文解讀]Multi-View 3D Object Detection Network for Autonomous Driving