VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
论文： 在3D点云中准确检测对象是自主导航，客房服务机器人和增强/虚拟现实的中心问题。为了将高度稀疏的LiDAR点云与区域提议网络（RPN）进行接口，大多数现有工作都集中在手工制作的特征表示上，例如鸟瞰图。在这项工作中，消除了对3D点云进行手动特征工程的需求，并提出了VoxelNet，这是一种通用的3D检测网络，它将特征提取和边界框预测统一为一个端到端可训练的单阶段深层网络。将点云转换为等距的3D
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- 1. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 2. VoxelNet：End-to-End Learning for Point Cloud Based 3D Object Detection阅读笔记
- 3. 3D Object Detection——Voxel based methods
- 4. 三维目标检测论文阅读：VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 5. 【文章阅读】STD：Sparse-to-Dense 3D Object Detector for Point Cloud
- 6. Structure Aware Single-stage 3D Object Detection from Point Cloud
- 7. 3d fully convolutional network for vehicle detection in point cloud
- 8. RepPoints: Point Set Representation for Object Detection
- 9. PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
- 10. Point Cloud 2019
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相关文章
- 1. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 2. VoxelNet：End-to-End Learning for Point Cloud Based 3D Object Detection阅读笔记
- 3. 3D Object Detection——Voxel based methods
- 4. 三维目标检测论文阅读：VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- 5. 【文章阅读】STD：Sparse-to-Dense 3D Object Detector for Point Cloud
- 6. Structure Aware Single-stage 3D Object Detection from Point Cloud
- 7. 3d fully convolutional network for vehicle detection in point cloud
- 8. RepPoints: Point Set Representation for Object Detection
- 9. PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
- 10. Point Cloud 2019