MVIG-SJTU/pointSIFT

PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation

Created by Mingyang Jiang, Yiran Wu, Tianqi Zhao, Zelin Zhao, Cewu Lu (corresponding author).

Introduction

PointSIFT is a semantic segmentation framework for 3D point clouds. It is based on a simple module which extract featrues from neighbor points in eight directions. For more details, please refer to our arxiv paper.

PointSIFT is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, contact Cewu Lu.

Installation

In our experiment, All the codes are tested in Python3.5(If you use Python 2.7, please add some system paths), CUDA 8.0 and CUDNN 5.1.

1. Install TensorFlow (We use v1.4.1).
2. Install other python libraries like h5py
3. Compile TF operator (Similar to PointNet++). Firstly, you should find Tensorflow include path and library paths.

    import tensorflow as tf
    # include path
    print(tf.sysconfig.get_include())
    # library path 
    print(tf.sysconfig.get_lib())

Then, change the path in all the complie file, like tf_utils/tf_ops/sampling/tf_sampling_compile.sh Finally, compile the source file, we use tf_sampling as example.

    cd tf_utils/tf_ops/sampling
    chmod +x tf_sampling_compile.sh
    ./tf_sampling_compile.sh

Usage

If you want use our model in your own project. After compiling the TF operator, you can import it easily. Here shows a simple case.(we take batch_size * num_point * input_dim as input and get batch_size * num_point * output_dim as output)

import tensorflow as tf
# import our module
from tf_utils.pointSIFT_util import pointSIFT_module
# input coordinates
xyz = tf.tf.placeholder(tf.float32, shape=(batch_size, num_point, 3))
# input features
point_feature = tf.tf.placeholder(tf.float32, shape=(batch_size, num_point, input_dim)
# setting phases
is_training = tf.placeholder(dtype=tf.bool, shape=())
# setting searching radius (0.1 as an example)
radius = 0.1
_, out_feature, _ = pointSIFT_module(xyz, point_feature, radius, output_dim, is_training)

Training and evaluating on ScanNet

1. All the data can be download from here. They are the same as PointNet++.
2. Train the data:

python train_and_eval_scannet.py

If you have multiple GPU:

CUDA_VISIBLE_DEVICES=0,1,2,3 python train_and_eval_scannet.py --gpu_num=4

Citation

Please cite the paper in your publications if it helps your research:

@misc{1807.00652,
Author = {Mingyang Jiang and Yiran Wu and Tianqi Zhao and Zelin Zhao and Cewu Lu},
Title = {PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation},
Year = {2018},
Eprint = {arXiv:1807.00652},
}