VoxelNet：End-to-End Learning for Point Cloud Based 3D Object Detection阅读笔记
前言 本文首发于公众号【3D视觉工坊】，原文请见VoxelNet阅读笔记 主要思想和创新点 三维点云中目标的精确检测是许多应用中的核心问题，如自主导航、管家机器人和增强/虚拟现实等。为了将高度稀疏的激光雷达点云与区域建议网络（RPN）连接，大多数现有的工作都集中在手工制作的特征表示上，例如，鸟瞰图投影。 这项工作消除了人工特征工程对三维点云的需求，提出了VoxelNet，一种将特征提取和box预测
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相关文章
- 1. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 2. 论文阅读速记：PointRCNN：3D Object Proposal Generation and Detection from Point Cloud
- 3. 三维目标检测论文阅读：VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 4. 3D Object Detection——Voxel based methods
- 5. Feature Pyramid Networks for Object Detection 阅读笔记
- 6. 【文章阅读】STD：Sparse-to-Dense 3D Object Detector for Point Cloud
- 7. 论文阅读CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
- 8. Deep Hough Voting for 3D Object Detection in Point Clouds 论文笔记
- 9. 论文阅读笔记《RepMet Representative-based metric learning for classification and few-shot object detection》
- 10. A Simple Pooling-Based Design for Real-Time Salient Object Detection阅读笔记