3D Object Detection——Voxel based methods

时间 2020-12-30

标签 3D点云处理繁體版

原文原文链接

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection VoxelNet 端到端的3D目标检测网络，且仅使用3D LIDAR 点云数据就可以得到很好的精度。网络结构包含三个过程： 1. Feature learning network 构造3D 网格，对输入的点云数据栅格化。整个点云的大立方体的深度、高度

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1. 3D Object Detection——BEV-based methods
2. 【论文速览】PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
3. 一文读懂PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
4. Image-based 3D Object Reconstruction
5. Stereo R-CNN based 3D Object Detection for Autonomous Driving
6. Image-Based Object Spoofifing Detection
7. CornerNet-Lite: Efficient Keypoint-Based Object Detection
8. 解读：PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
9. Pillar-based Object Detection for Autonomous Driving
10. An Introduction to CNN based Object Detection
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1. 3D Object Detection——BEV-based methods
2. 【论文速览】PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
3. 一文读懂PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
4. Image-based 3D Object Reconstruction
5. Stereo R-CNN based 3D Object Detection for Autonomous Driving
6. Image-Based Object Spoofifing Detection
7. CornerNet-Lite: Efficient Keypoint-Based Object Detection
8. 解读：PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
9. Pillar-based Object Detection for Autonomous Driving
10. An Introduction to CNN based Object Detection

>>更多相关文章<<