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Mix3D: Out-of-Context Data Augmentation for 3D Scenes (3DV 2021)

Alexey Nekrasov*, Jonas Schult*, Or Litany, Bastian Leibe, Francis Engelmann

Mix3D is a data augmentation technique for 3D segmentation methods that improves generalization.


PyTorch Lightning Config: Hydra Code style: black


[Project Webpage] [arXiv] [Demo]


We also host a demo page, where you can upload your scene and in 30 minutes receive a prediction from the model! We created it to collect the most challenging scenes that could be captured in the wild and test the limits of the current SOTA models.


  • 18. April 2022: For the last Minkowski Engine release use branch or pull request
  • 12. October 2021: Code released.
  • 6. October 2021: Mix3D accepted for oral presentation at 3DV 2021. Paper on [arXiv].
  • 30. July 2021: Mix3D ranks 1st on the ScanNet semantic labeling benchmark.


Running the code

This repository contains the code for the analysis experiments of section 4.2. Motivation and Analysis Experiments from the paper For the ScanNet benchmark and Table 1 (main paper) we use the original SpatioTemporalSegmentation-Scannet code. To add Mix3D to the original MinkowskiNet codebase, we provide the patch file SpatioTemporalSegmentation.patch. With the patch file kpconv_tensorflow_mix3d.patch, you can add Mix3D to the official TensorFlow code release of KPConv on ScanNet and S3DIS. Analogously, you can patch the official PyTorch reimplementation of KPConv with the patch file kpconv_pytorch_mix3d.patch. Check the supplementary for more details.

Code structure

├── mix3d
│   ├──
│   ├──     <- the main file
│   ├── conf            <- hydra configuration files
│   ├── datasets
│   │   ├──       <- outdoor dataset
│   │   ├── preprocessing       <- folder with preprocessing scripts
│   │   ├──       <- indoor dataset
│   │   └──        <- code for mixing point clouds
│   ├── logger
│   ├── models      <- MinkowskiNet models
│   ├── trainer
│   │   ├──
│   │   └──      <- train loop
│   └── utils
├── data
│   ├── processed       <- folder for preprocessed datasets
│   └── raw     <- folder for raw datasets
├── scripts
│   ├── experiments
│   │   └── 1000_scene_merging.bash
│   ├── init.bash
│   ├── local_run.bash
│   ├── preprocess_matterport.bash
│   ├── preprocess_rio.bash
│   ├── preprocess_scannet.bash
│   └── preprocess_semantic_kitti.bash
├── docs
├── dvc.lock
├── dvc.yaml        <- dvc file to reproduce the data
├── poetry.lock
├── pyproject.toml      <- project dependencies
├── saved       <- folder that stores models and logs
└── SpatioTemporalSegmentation-ScanNet.patch        <- patch file for original repo


The main dependencies of the project are the following:

python: 3.7
cuda: 10.1

For others, the project uses the poetry dependency management package. Everything can be installed with the command:

poetry install

Check scripts/init.bash for more details.

Data preprocessing

After the dependencies are installed, it is important to run the preprocessing scripts. They will bring scannet, matterport, rio, semantic_kitti datasets to a single format. By default, the scripts expect to find datsets in the data/raw/ folder. Check scripts/preprocess_*.bash for more details.

dvc repro scannet # matterport, rio, semantic_kitti

This command will run the preprocessing for scannet and will save the result using the dvc data versioning system.

Training and testing

Train MinkowskiNet on the scannet dataset without Mix3D with a voxel size of 5cm:

poetry run train

Train MinkowskiNet on the scannet dataset with Mix3D with a voxel size of 5cm:

poetry run train data/collation_functions=voxelize_collate_merge


  title     = {{Mix3D: Out-of-Context Data Augmentation for 3D Scenes}},
  author    = {Nekrasov, Alexey and Schult, Jonas and Litany, Or and Leibe, Bastian and Engelmann, Francis},
  booktitle = {{International Conference on 3D Vision (3DV)}},
  year      = {2021}


Mix3D: Out-of-Context Data Augmentation for 3D Scenes (3DV 2021 Oral)







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