論文閱讀速記：PointRCNN：3D Object Proposal Generation and Detection from Point Cloud
PointRCNN 兩階段： 一、 通過點雲分割自下而上生成3D提案 二、點雲區域池化 三、規範3D邊界框優化 原始點雲的3D目標檢測，只用點雲作爲輸入。提出一種新的3D物體檢測器，用於從原始點雲中檢測3D物體。所提出的Stage-1網絡以自下而上的方式直接從點雲生成3D方案，比以前的方案生成方法具有更高的召回率。Stage-2網絡將語義特徵和局部空間特徵結合起來，在規範座標中對提案進行了優化。此
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相關文章
- 1. Joint 3D Proposal Generation and Object Detection from View Aggregation論文學習
- 2. （動工中）論文梳理 —— Joint 3D Proposal Generation and Object Detection from View Aggregation
- 3. 【論文閱讀】【三維目標檢測】Structure Aware Single-stage 3D Object Detection from Point Cloud
- 4. VoxelNet：End-to-End Learning for Point Cloud Based 3D Object Detection閱讀筆記
- 5. Structure Aware Single-stage 3D Object Detection from Point Cloud
- 6. HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
- 7. 【論文閱讀】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
- 8. 【文章閱讀】STD：Sparse-to-Dense 3D Object Detector for Point Cloud
- 9. 《論文閱讀》ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes
- 10. USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds 2019 論文筆記