Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving
Peiliang Li、Tong Qin、沈劭劼 香港科技大学 摘要 我们提出一种基于立体相机的方法来追踪复杂自动驾驶环境中相机的运动和3D语义对象。取代了直接还原3D立体轮廓的端到端方法，我们提出使用简易标记的2D检测和离散视点分类与一个轻量级的语义推断方法一同去获得粗略的3D对象测量值。基于在复杂环境中鲁棒的半自动对象感知相机姿态追踪，结合我们新的动态对象集束调整方法去融合时间稀疏对应特征和3
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相关文章
- 1. Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 2. 论文阅读CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 3. Multi-View 3D Object Detection Network for Autonomous Driving
- 4. Pillar-based Object Detection for Autonomous Driving
- 5. [论文解读]Multi-View 3D Object Detection Network for Autonomous Driving
- 6. 解读ECCV2020：Pillar-based Object Detection for Autonomous Driving
- 7. MV3D：Multi-View 3D Object Detection Network for Autonomous Driving（翻译）
- 8. Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving Yurong
- 9. 【3D 目标检测】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
- 10. MODNet-Motion and Appearance based Moving Object Detection Network for Autonomous Driving