This is updated version of PointNet++ with Python 3 and tested under TensorFlow 1.13.
The original PointNet++ with Python 2.7 is at PointNet2.
Created by Charles R. Qi, Li (Eric) Yi, Hao Su, Leonidas J. Guibas from Stanford University.
Install TensorFlow. The code is tested under TF1.13 GPU version and Python 3.7 on Ubuntu 16.04. There are also some dependencies for a few Python libraries for data processing and visualizations like cv2, h5py etc.
The TF operators are included under tf_ops, you need to compile them (check tf_xxx_compile.sh under each ops subfolder) first. Update nvcc and python path if necessary. The code is tested under TF1.13.0.
To train a PointNet++ model to classify ModelNet40 shapes (using point clouds with XYZ coordinates):
python train.py
To see all optional arguments for training:
python train.py -h
If you have multiple GPUs on your machine, you can also run the multi-GPU version training (our implementation is similar to the tensorflow cifar10 tutorial):
CUDA_VISIBLE_DEVICES=0,1 python train_multi_gpu.py --num_gpus 2
After training, to evaluate the classification accuracies (with optional multi-angle voting):
python evaluate.py --num_votes 12
Side Note: For the XYZ+normal experiment reported in the PointNet++ paper: (1) 5000 points are used and (2) a further random data dropout augmentation is used during training (see commented line after augment_batch_data in train.py and (3) the model architecture is updated such that the nsample=128 in the first two set abstraction levels, which is suited for the larger point density in 5000-point samplings.
To use normal features for classification: You can get our sampled point clouds of ModelNet40 (XYZ and normal from mesh, 10k points per shape) here (1.6GB). Move the uncompressed data folder to data/modelnet40_normal_resampled
To train a model to segment object parts for ShapeNet models:
cd part_seg
python train.py
Preprocessed ShapeNetPart dataset (XYZ, normal and part labels) can be found here (674MB). Move the uncompressed data folder to data/shapenetcore_partanno_segmentation_benchmark_v0_normal
See scannet/README and scannet/train.py for details.
We have provided a handy point cloud visualization tool under utils. Run sh compile_render_balls_so.sh to compile it and then you can try the demo with python show3d_balls.py The original code is from here.
You can refer to here on how to prepare your own HDF5 files for either classification or segmentation. Or you can refer to modelnet_dataset.py on how to read raw data files and prepare mini-batches from them. A more advanced way is to use TensorFlow's dataset APIs, for which you can find more documentations here.
Our code is released under MIT License (see LICENSE file for details).
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation by Qi et al. (CVPR 2017 Oral Presentation). Code and data released in GitHub.
- Frustum PointNets for 3D Object Detection from RGB-D Data by Qi et al. (CVPR 2018) A novel framework for 3D object detection with RGB-D data. Based on 2D boxes from a 2D object detector on RGB images, we extrude the depth maps in 2D boxes to point clouds in 3D space and then realize instance segmentation and 3D bounding box estimation using PointNet/PointNet++. The method proposed has achieved first place on KITTI 3D object detection benchmark on all categories (last checked on 11/30/2017). Code and data release TBD.