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Pointnet2.ScanNet

PointNet++ Semantic Segmentation on ScanNet in PyTorch with CUDA acceleration based on the original PointNet++ repo and the PyTorch implementation with CUDA

Performance

The semantic segmentation results in percentage on the ScanNet train/val split in data/.

If you want to play around with the pre-trained model, please download the zip file and unzip it under outputs/.

Installation

Requirements

• Linux (tested on Ubuntu 14.04/16.04)
• Python 3.6+
• PyTorch 1.8
• TensorBoardX

Please run conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch to install PyTorch 1.8 and run pip install -r requirements.txt to install other required packages.

Install CUDA accelerated PointNet++ library

Install this library by running the following command:

cd pointnet2
python setup.py install

Configure

Change the path configurations for the ScanNet data in lib/config.py

Prepare multiview features (optional)

1. Download the ScanNet frames here (~13GB) and unzip it under the project directory.

2. Extract the multiview features from ENet:

python scripts/compute_multiview_features.py

3. Generate the projection mapping between image and point cloud

python scripts/compute_multiview_projection.py

4. Project the multiview features from image space to point cloud

python scripts/project_multiview_features.py

Note you might need ~100GB RAM to train the model with multiview features

Usage

Preprocess ScanNet scenes

Parse the ScanNet data into *.npy files and save them in preprocessing/scannet_scenes/

python preprocessing/collect_scannet_scenes.py

Sanity check

Don't forget to visualize the preprocessed scenes to check the consistency

python preprocessing/visualize_prep_scene.py --scene_id <scene_id>

The visualized <scene_id>.ply is stored in preprocessing/label_point_clouds/ - Drag that file into MeshLab and you'll see something like this:

train

Train the PointNet++ semantic segmentation model on ScanNet scenes with raw RGB values and point normals (for more training options, see python scripts/train.py -h)

python scripts/train.py --use_color --use_normal --use_msg

The trained models and logs will be saved in outputs/<time_stamp>/

eval

Evaluate the trained models and report the segmentation performance in point accuracy, voxel accuracy and calibrated voxel accuracy

python scripts/eval.py --folder <time_stamp>

Note that all model options must match the ones used for training.

vis

Visualize the semantic segmentation results on points in a given scene

python scripts/visualize.py --folder <time_stamp> --scene_id <scene_id>

Note that all model options must match the ones used for training.

The generated <scene_id>.ply is stored in outputs/<time_stamp>/preds - Drag that file into MeshLab and you'll see something like the one below. See the class palette here

Changelog

• 07/29/2021 Upgrade to PyTorch 1.8 & fix existing issues
• 03/29/2020 Release the code

TODOs

• Release all pretrained models
• Upgrade to PyTorch 1.8
• Fix issues with loading pre-trained models

Acknowledgement

• charlesq34/pointnet2: Paper author and official code repo.
• erikwijmans/Pointnet2_PyTorch: Initial work of PyTorch implementation of PointNet++ with CUDA acceleration.