[论文解读]Multi-View 3D Object Detection Network for Autonomous Driving
问题引入 在无人驾驶场景中，物体的3D检测(或者说是bounding-box)的重要性不言而喻。从2D检测到3D检测，主要的难点是提取深度信息，因此总体上可以将其分为四种，分别为： 基于单目图像(Mono) 基于双目图像(Stereo) 基于雷达(LIDAR) 基于融合网络 这篇论文就是一篇较为出色的融合网络的范例。后期和清华的老师沟通得知，这篇论文的算法正在被产业化，因此源码在一段时间内不会透露
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- 1. Multi-View 3D Object Detection Network for Autonomous Driving
- 2. 论文阅读CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 3. 解读ECCV2020：Pillar-based Object Detection for Autonomous Driving
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- 6. 【3D目标检测】Multi-View 3D Object Detection Network for Autonomous Driving论文综述（2016）
- 7. MV3D：Multi-View 3D Object Detection Network for Autonomous Driving（翻译）
- 8. MODNet-Motion and Appearance based Moving Object Detection Network for Autonomous Driving
- 9. 每天一篇论文 373/1000 PSEUDO-LIDAR++:ACCURATE DEPTH FOR 3D OBJECT DETECTION IN AUTONOMOUS DRIVING
- 10. 每天一篇论文 324/365RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving
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相关文章
- 1. Multi-View 3D Object Detection Network for Autonomous Driving
- 2. 论文阅读CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 3. 解读ECCV2020：Pillar-based Object Detection for Autonomous Driving
- 4. Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 5. Pillar-based Object Detection for Autonomous Driving
- 6. 【3D目标检测】Multi-View 3D Object Detection Network for Autonomous Driving论文综述（2016）
- 7. MV3D：Multi-View 3D Object Detection Network for Autonomous Driving（翻译）
- 8. MODNet-Motion and Appearance based Moving Object Detection Network for Autonomous Driving
- 9. 每天一篇论文 373/1000 PSEUDO-LIDAR++:ACCURATE DEPTH FOR 3D OBJECT DETECTION IN AUTONOMOUS DRIVING
- 10. 每天一篇论文 324/365RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving