Welcome to the world of Mask3D, the Mask Transformer designed specifically for 3D instance segmentation! If you are venturing into 3D semantic segmentation, you’re likely to be excited about the state-of-the-art performance that Mask3D achieves on datasets such as ScanNet, ScanNet200, S3DIS, and STPLS3D. In this guide, we’ll walk you through the essential steps to get started with Mask3D, highlight some key features, and troubleshoot common issues you may encounter.
Getting Started with Mask3D
Before diving into our first steps, let’s lay down our objectives. This guide will cover:
- Setting up the environment for Mask3D
- Data preprocessing
- Training and testing the model
1. Setting Up the Environment
First, you’ll want to establish a proper environment to execute Mask3D. It is adaptable with a codebase inspired by Mix3D, meaning it’s modularized for ease of use. Here’s how to set it up:
- Ensure you have Python version 3.10.9 and CUDA 11.3.
- Create a conda environment and install the required dependencies:
conda env create -f environment.yml
conda activate mask3d_cuda113
pip3 install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
pip3 install torch-scatter -f https://data.pyg.org/whl/torch-1.12.1+cu113.html
pip3 install git+https://github.com/facebookresearch/detectron2.git@710e7795d0eeadf9def0e7ef957eea13532e34cf --no-deps
mkdir third_party
cd third_party
git clone --recursive https://github.com/NVIDIA/MinkowskiEngine
cd MinkowskiEngine
git checkout 02fc608bea4c0549b0a7b00ca1bf15dee4a0b228
python setup.py install --force_cuda --blas=openblas
cd ..
git clone https://github.com/ScanNet/ScanNet.git
cd ScanNetSegmentator
git checkout 3e5726500896748521a6ceb81271b0f5b2c0e7d2
make
cd ..
pip3 install pytorch-lightning==1.7.2
2. Data Preprocessing
After setting up the environment, it’s time to preprocess datasets like ScanNet, S3DIS, and STPLS3D. Here’s how:
- For ScanNet and ScanNet200, apply Felzenswalb and Huttenlocher’s Graph Based Image Segmentation algorithm.
- Use the preprocessing script as follows:
python -m datasets.preprocessing.scannet_preprocessing preprocess --data_dir=PATH_TO_RAW_SCANNET_DATASET --save_dir=data/processed/scannet --git_repo=PATH_TO_SCANNET_GIT_REPO --scannet200=false
python -m datasets.preprocessing.stpls3d_preprocessing preprocess --data_dir=PATH_TO_STPLS3D --save_dir=data/processed/stpls3d
3. Training and Testing
Now that the datasets are ready, let’s get Mask3D trained and tested.
- To train Mask3D on the ScanNet dataset, use the command:
python main_instance_segmentation.py
python main_instance_segmentation.py general.checkpoint=PATH_TO_CHECKPOINT.ckpt general.train_mode=false
Understanding Mask3D’s Architecture Like a Puzzle
Think of Mask3D as an intricate 3D puzzle. Each piece has a specific role in forming the complete picture:
- The preprocessors are like the edges of the puzzle, setting the boundaries.
- The main instance segmentation script acts as the core piece that connects everything together.
- Finally, the individual datasets resemble puzzle pieces that, when placed correctly, reveal cohesive instances in 3D space.
Troubleshooting Common Issues
While using Mask3D, you may occasionally run into some hiccups. Here are common problems and their solutions:
- Error when setting up the environment: Ensure you have the necessary dependencies installed, particularly the correct Python version.
- Data inconsistencies: Double-check dataset paths and preprocess thoroughly.
- CUDA-related errors: Confirm that the proper CUDA version is installed and accessible.
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Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.