The main code is from PointNet GitHub Repo
You can download the dataset from here.
All files are provided as numpy .npy files. Each file contains a N x F matrix, where N is the number of points in a scene and F is the number of features per point, in this case F=7. The features are XYZRGBL, the 3D XYZ position, the RGB color and the ground truth semantic label L. Each file is for a scene.
Once you have downloaded and prepared data, to start training use main.ipynb.
For data visualization you can use vis_data_vispy.py file.
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space by Qi et al. (NIPS 2017) A hierarchical feature learning framework on point clouds. The PointNet++ architecture applies PointNet recursively on a nested partitioning of the input point set. It also proposes novel layers for point clouds with non-uniform densities.
- Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds by Engelmann et al. (ICCV 2017 workshop). This work extends PointNet for large-scale scene segmentation.
- PCPNET: Learning Local Shape Properties from Raw Point Clouds by Guerrero et al. (arXiv). The work adapts PointNet for local geometric properties (e.g. normal and curvature) estimation in noisy point clouds.
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection by Zhou et al. from Apple (arXiv) This work studies 3D object detection using LiDAR point clouds. It splits space into voxels, use PointNet to learn local voxel features and then use 3D CNN for region proposal, object classification and 3D bounding box estimation.
- Frustum PointNets for 3D Object Detection from RGB-D Data by Qi et al. (arXiv) A novel framework for 3D object detection with RGB-D data. The method proposed has achieved first place on KITTI 3D object detection benchmark on all categories (last checked on 11/30/2017).