The code is based on PointNet, PointNet++, SpiderCNN, PointConv and SceneEncoder. Please install TensorFlow, and follow the instruction in PointNet++ to compile the customized TF operators in the tf_ops directory. Specifically, you may need to check tf_xxx_compile.sh under each tf_ops subfolder, and modify ${CUDA_PATH} and the version of python if necessary.
For example, you need to change the shell script tf_ops/3d_interpolation/tf_interpolate_compile.sh from
CUDA_PATH=/usr/local/cuda
to
CUDA_PATH=/usr/local/cuda-9.0
if you use CUDA 9.0.
Additionally, you need to specifically change the command
TF_INC=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_include())')
to
TF_INC=$(python3 -c 'import tensorflow as tf; print(tf.sysconfig.get_include())')
if your default python is python2.
The code has been tested with Python 3.6, TensorFlow 1.13.1, CUDA 10.0 and cuDNN 7.3 on Ubuntu 18.04.
To install some of required package, run:
pip install -r requirements.txt
Please download the ScanNetv2 dataset from here, and see scannet/README for details of preprocessing.
To train a model to segment Scannet Scenes:
CUDA_VISIBLE_DEVICES=0 python train_scannet_IoU.py --model scene_encoder_rsl --log_dir scannet_ --batch_size 8
After training, to generate test results to dump_%s directory:
CUDA_VISIBLE_DEVICES=0 python evaluate_scannet.py --model scene_encoder_rsl --batch_size 8 --model_path scannet_%s --with_rgb
Then, to upload the results to the ScanNetv2 benchmark server:
zip out.zip dump_%s/scene*
(Optional) To visualize the results on validation dataset:
CUDA_VISIBLE_DEVICES=0 python visualize_scene.py --model scene_encoder_rsl --batch_size 8 --model_path scannet_%s --ply_path DataSet/ScanNetv2/scans --with_rgb
Modify the model_path to your .ckpt file path and the ply_path to the original ScanNetv2 ply file.
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Thanks:
This repository is released under MIT License (see LICENSE file for details).