PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
PointPillars: Fast Encoders for 3D Object Detection from Point Clouds 简介 3D目标检测整体框架 数据增强 生成伪图像 基础网络 检测头 Loss 简介 考虑到点云数据的不规则，将点云编码为适合下游检测流程的格式，是一个首先需要处理的问题。一般有两种处理方式： 固定编码器，这种编码器往往速度快但是以牺牲精度为前提 在数据中学习的
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相关文章
- 1. PIXOR Real-time 3D Object Detection from Point Clouds
- 2. 【3D计算机视觉】Deep Hough Voting for 3D Object Detection in Point Clouds
- 3. Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient CNN论文综述（2016）
- 4. Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
- 5. 【论文阅读】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
- 6. CVPR2018_Frustum PointNets for 3D Object Detection from RGB-D Data
- 7. 3D Point Clouds-1
- 8. Complex-YOLO: An Euler-Region-Proposal for Real-time 3D Object Detection on Point Clouds
- 9. Deep Hough Voting for 3D Object Detection in Point Clouds 论文笔记
- 10. 【论文阅读】【3D目标检测】StarNet: Targeted Computation for Object Detection in Point Clouds