STD：Sparse-to-Dense 3D Object Detector for Point Cloud(腾讯&香港大学)
点击上方“3D视觉工坊”，选择“星标” 干货第一时间送达 原文地址：https://arxiv.org/abs/1907.10471 主要思想 本文提出了一种新的两阶段3D目标检测框架，称之为稀疏到稠密三维目标检测框架（STD）。第一个阶段是自下而上的proposal生成网络，该网络使用原始点云作为输入，通过为每个点播种新的球形anchor来生成准确的proposal。与以前的工作相比，该算法具有
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- 1. 【文章阅读】STD：Sparse-to-Dense 3D Object Detector for Point Cloud
- 2. Structure Aware Single-stage 3D Object Detection from Point Cloud
- 3. PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
- 4. VoxelNet：End-to-End Learning for Point Cloud Based 3D Object Detection阅读笔记
- 5. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 6. 【3D计算机视觉】Deep Hough Voting for 3D Object Detection in Point Clouds
- 7. 香港中文大学 pose
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相关文章
- 1. 【文章阅读】STD：Sparse-to-Dense 3D Object Detector for Point Cloud
- 2. Structure Aware Single-stage 3D Object Detection from Point Cloud
- 3. PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
- 4. VoxelNet：End-to-End Learning for Point Cloud Based 3D Object Detection阅读笔记
- 5. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 6. 【3D计算机视觉】Deep Hough Voting for 3D Object Detection in Point Clouds
- 7. 香港中文大学 pose
- 8. 3d fully convolutional network for vehicle detection in point cloud
- 9. A GRAPH-CNN FOR 3D POINT CLOUD CLASSIFICATION
- 10. Point Cloud 2019