post2web / PointCNN

CNN Generalized for Consuming Point Cloud Data

PointCNN

Created by Yangyan Li, Rui Bu, Mingchao Sun, and Baoquan Chen from Shandong University.

Introduction

PointCNN is a simple and general framework for feature learning from point cloud, which refreshed five benchmark records in point cloud processing, including:

• classification accuracy on ModelNet40 (91.7%)
• classification accuracy on ScanNet (77.9%)
• segmentation part averaged IoU on ShapeNet Parts (86.13%)
• segmentation mean IoU on S3DIS (62.74%)
• per voxel labelling accuracy on ScanNet (85.1%)

PointCNN achieved 84.4% accuracy on ModelNet40 classification with only 32 input points, which outperforms PointNet and PointNet++ with a 18.3% accuracy gap, making PointCNN quite promising for real time recognition applications with low resolution point cloud input, such as autonomous driving, as well as robotics in general.

See our PointCNN paper on arXiv for more details.

Code Organization

The core X-Conv and PointCNN architecture are defined in pointcnn.py.

The network/training/data augmentation hyper parameters for classification tasks are defined in pointcnn_cls, for segmentation tasks are defined in pointcnn_seg.

Explanation of X-Conv and X-DeConv Parameters

Take the xconv_params and xdconv_params from shapenet_x8_2048_fps.py for example:

# K, D, P, C
xconv_params = [(8, 1, -1, 32 * x),
                (12, 2, 768, 32 * x),
                (16, 2, 384, 64 * x),
                (16, 6, 128, 128 * x)]

# K, D, pts_layer_idx, qrs_layer_idx
xdconv_params = [(16, 6, 3, 2),
                 (12, 6, 2, 1),
                 (8, 6, 1, 0),
                 (8, 4, 0, 0)]

Each element in xconv_params is a tuple of (K, D, P, C), where K is the neighborhood size, D is the dilation rate, P is the representative point number in the output (-1 means all input points are output representative points), and C is the output channel number. Each element specifies the parameters of one X-Conv layer, and they are stacked to create a deep network.

Each element in xdconv_params is a tuple of (K, D, pts_layer_idx, qrs_layer_idx), where K and D have the same meaning as that in xconv_params, pts_layer_idx specifies the output of which X-Conv layer (from the xconv_params) will be the input of this X-DeConv layer, and qrs_layer_idx specifies the output of which X-Conv layer (from the xconv_params) will be forwarded and fused with the output of this X-DeConv layer. The P and C parameters of this X-DeConv layer is also determined by qrs_layer_idx. Similarly, each element specifies the parameters of one X-DeConv layer, and they are stacked to create a deep network.

PointCNN Usage

PointCNN is implemented and tested with Tensorflow 1.4 in python3 scripts. Tensorflow before 1.3 version is not recommended, as Tensoflow 1.3 introduced a notable speedup in top_k operation, which PointCNN heavily depends on for nearest neighbor query. It has dependencies on some python packages such as transforms3d, h5py, plyfile, and maybe more if it complains. Install these packages before the use of PointCNN.

Here we list the commands for training/evaluating PointCNN on classification and segmentation tasks on multiple datasets.

• Classification

  • ModelNet40

  cd data_conversions
  python3 ./download_datasets.py -d modelnet
  cd ../pointcnn_cls
  ./train_val_modelnet.sh -g 0 -x modelnet_x2_l4
  

  • ScanNet

  Please refer to http://www.scan-net.org/ for downloading ScanNet task data and scannet_labelmap, and refer to https://github.com/ScanNet/ScanNet/tree/master/Tasks/Benchmark for downloading ScanNet benchmark files:

  scannet_dataset_download

  |_ data

  |_ scannet_labelmap

  |_ benchmark

  cd ../data/scannet/scannet_dataset_download/
  mv ./scannet_labelmap/scannet-labels.combined.tsv ../benchmark/
  
  #./pointcnn_root
  cd ../../../pointcnn/data_conversions
  python scannet_extract_obj.py -f ../../data/scannet/scannet_dataset_download/data/ -b ../../data/scannet/scannet_dataset_download/benchmark/ -o ../../data/scannet/cls/
  python prepare_scannet_cls_data.py -f ../../data/scannet/cls/
  cd ../pointcnn_cls/
  ./train_val_scannet.sh -g 0 -x scannet_x2_l4.py
  

  • tu_berlin

  cd data_conversions
  python3 ./download_datasets.py -d tu_berlin
  python3 ./prepare_tu_berlin_data.py -f ../../data/tu_berlin/ -a
  cd ../pointcnn_cls
  ./train_val_tu_berlin.sh -g 0 -x tu_berlin_x2_l5
  

  • quick_darw

  Note that the training/evaluation of quick_draw requires LARGE RAM, as we load all stokes into RAM and converting them into point cloud on-the-fly.

  cd data_conversions
  python3 ./download_datasets.py -d quick_draw
  cd ../pointcnn_cls
  ./train_val_quick_draw.sh -g 0 -x quick_draw_full_x2_l6
  

  • MNIST

  cd data_conversions
  python3 ./download_datasets.py -d mnist
  python3 ./prepare_mnist_data.py -f ../../data/mnist
  cd ../pointcnn_cls
  ./train_val_mnist.sh -g 0 -x mnist_x2_l5
  

  • CIFAR-10

  cd data_conversions
  python3 ./download_datasets.py -d cifar10
  python3 ./prepare_cifar10_data.py -f ../../data/cifar10
  cd ../pointcnn_cls
  ./train_val_cifar10.sh -g 0 -x cifar10_x2_l4
  

• Segmentation

  We use farthest point sampling (the implementation from PointNet++) in segmentation tasks. Compile FPS before the training/evaluation:

  cd sampling
  bash tf_sampling_compile.sh
  

  • ShapeNet

  cd data_conversions
  python3 ./download_datasets.py -d shapenet_partseg
  python3 ./prepare_partseg_data.py -f ../../data/shapenet_partseg
  cd ../pointcnn_seg
  ./train_val_shapenet.sh -g 0 -x shapenet_x8_2048_fps
  ./test_shapenet.sh -g 0 -x shapenet_x8_2048_fps -l ../../models/seg/pointcnn_seg_shapenet_x8_2048_fps_xxxx/ckpts/iter-xxxxx -r 10
  cd ../evaluation
  python3 eval_shapenet_seg.py -g ../../data/shapenet_partseg/test_label -p ../../data/shapenet_partseg/test_data_pred_10
  

  • S3DIS

  Please refer to data_conversions for downloading S3DIS, then:

  cd data_conversions/split_data
  python3 s3dis_prepare_label.py
  python3 s3dis_split.py
  cd ..
  python3 prepare_multiChannel_seg_data.py -f ../../data/S3DIS/out_part_rgb/ -c 6
  mv S3DIS_files/* ../../data/S3DIS/out_part_rgb/
  ./train_val_s3dis.sh -g 0 -x s3dis_x8_2048_fps_k16
  ./test_s3dis.sh -g 0 -x s3dis_x8_2048_fps_k16 -l ../../models/seg/s3dis_x8_2048_fps_k16_xxxx/ckpts/iter-xxxxx -r 4
  cd ../evaluation
  python3 s3dis_upsampling.py
  python3 eval_s3dis.py
  

  Please notice that these command just for Area1 validation, after modify the train val path in train_val_s3dis.sh, test_s3dis.sh and s3dis_upsampling.py, you can get other Area results.

  • ScanNet

  Please refer to data_conversions for downloading ScanNet, then:

  cd data_conversions/split_data
  python3 scannet_split.py
  cd ..
  python3 prepare_multiChannel_seg_data.py -f ../../data/scannet/scannet_split_dataset/
  cd ../pointcnn_seg
  ./train_val_scannet.sh -g 0 -x scannet_x8_2048_k8_fps
  ./test_scannet.sh -g 0 -x scannet_x8_2048_k8_fps -l ../../models/seg/pointcnn_seg_scannet_x8_2048_k8_fps_xxxx/ckpts/iter-xxxxx -r 4
  cd ../evaluation
  python3 eval_scannet.py
  

  • Semantic3D

  cd data_conversions
  bash download_semantic3d.sh
  bash un7z_semantic3d.sh
  mkdir ../../data/semantic3d/val
  mv ../../data/semantic3d/train/bildstein_station3_xyz_intensity_rgb.txt ../../data/semantic3d/train/domfountain_station2_xyz_intensity_rgb.txt ../../data/semantic3d/train/sg27_station4_intensity_rgb.txt ../../data/semantic3d/train/untermaederbrunnen_station3_xyz_intensity_rgb.txt ../../data/semantic3d/val
  cd split_data
  python3 semantic3d
  cd ../pointcnn_seg
  ./train_val_semantic3d.sh -g 0 -x semantic3d_x8_2048_k16