论文阅读速记：PointRCNN：3D Object Proposal Generation and Detection from Point Cloud
PointRCNN 两阶段： 一、 通过点云分割自下而上生成3D提案 二、点云区域池化 三、规范3D边界框优化 原始点云的3D目标检测，只用点云作为输入。提出一种新的3D物体检测器，用于从原始点云中检测3D物体。所提出的Stage-1网络以自下而上的方式直接从点云生成3D方案，比以前的方案生成方法具有更高的召回率。Stage-2网络将语义特征和局部空间特征结合起来，在规范坐标中对提案进行了优化。此
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相关文章
- 1. Joint 3D Proposal Generation and Object Detection from View Aggregation论文学习
- 2. （动工中）论文梳理 —— Joint 3D Proposal Generation and Object Detection from View Aggregation
- 3. 【论文阅读】【三维目标检测】Structure Aware Single-stage 3D Object Detection from Point Cloud
- 4. VoxelNet：End-to-End Learning for Point Cloud Based 3D Object Detection阅读笔记
- 5. Structure Aware Single-stage 3D Object Detection from Point Cloud
- 6. HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
- 7. 【论文阅读】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
- 8. 【文章阅读】STD：Sparse-to-Dense 3D Object Detector for Point Cloud
- 9. 《论文阅读》ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes
- 10. USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds 2019 论文笔记