Project Page | Paper | Video | Models | Sample Data
Zak Murez, Tarrence van As, James Bartolozzi, Ayan Sinha, Vijay Badrinarayanan, and Andrew Rabinovich
We provide a Colab Notebook to try inference.
We provide a docker image Docker/Dockerfile with all the dependencies.
Or you can install them yourself:
conda install -y pytorch=1.5.0 torchvision=0.6.0 cudatoolkit=10.2 -c pytorch
conda install opencv
pip install \
open3d>=0.10.0.0 \
trimesh>=3.7.6 \
pyquaternion>=0.9.5 \
pytorch-lightning>=0.8.5 \
pyrender>=0.1.43
python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.5/index.html
For 16bit mixed precision (default training setting) you will also need NVIDIA apex
git clone https://github.com/NVIDIA/apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./apex
For headless rendering with pyrender (used for evaluation) see installation instructions here.
For inference with COLMAP see installation instructions here.
(If you have problems running the code try using the exact versions specified... for example the pytorch-lightning API has not settled yet).
We provide a small sample scene for easy download and rapid inference.
Download and extract the data to DATAROOT. The directory structure should look like:
DATAROOT
└───sample
│ └───sample1
│ │ intrinsics.txt
│ └───color
│ │ │ 00000001.jpg
│ │ │ 00000002.jpg
│ │ │ ...
│ └───pose
│ │ 00000001.txt
│ │ 00000002.txt
│ │ ...
Next run our data preperation script which parses the raw data format into our common json format (more info here)
(note that we store our derivered data in a seperate folder METAROOT to prevent pollution of the original data).
python prepare_data.py --path DATAROOT --path_meta METAROOT --dataset sample
Download and extract Scannet by following the instructions provided at http://www.scan-net.org/. The directory structure should look like:
DATAROOT
└───scannet
│ └───scans
│ | └───scene0000_00
│ | └───color
│ | │ │ 0.jpg
│ | │ │ 1.jpg
│ | │ │ ...
│ | │ ...
│ └───scans_test
│ └───color
│ │ │ 0.jpg
│ │ │ 1.jpg
│ │ │ ...
│ │ ...
Next run our data preperation script which parses the raw data format into our common json format (more info here)
(note that we store our derivered data in a seperate folder METAROOT to prevent pollution of the original data).
This script also generates the ground truth TSDFs using TSDF Fusion.
python prepare_data.py --path DATAROOT --path_meta METAROOT --dataset scannet
This will take a while (a couple hours on 8 Quadro RTX 6000's)... if you have multiple gpus you can use the --i and --n flags to run in parallel
python prepare_data.py --path DATAROOT --path_meta METAROOT --dataset scannet --i 0 --n 4 &
python prepare_data.py --path DATAROOT --path_meta METAROOT --dataset scannet --i 1 --n 4 &
python prepare_data.py --path DATAROOT --path_meta METAROOT --dataset scannet --i 2 --n 4 &
python prepare_data.py --path DATAROOT --path_meta METAROOT --dataset scannet --i 3 --n 4 &
Note that if you do not plan to train you can prepare just the test set using the --test flag.
To use your own data you will need to put it in the same format as the sample data, or implement your own version of something like sample.py. After that you can modify prepare_data.py to also prepare your data.
Note that the pretrained models are trained with Z-up metric coordinates and do not generalize to other coordinates (this means that the scale and 2 axes of the orientation ambiguity of SFM must be resolved prior to using the poses).
Once you have downloaded and prepared the data (as described above) you can run inference using our pretrained model (download) or by training your own (see below).
To run on the sample scene use:
python inference.py --model results/release/semseg/final.ckpt --scenes METAROOT/sample/sample1/info.json
If your GPU does not have enough memory you can reduce voxel_dim (at the cost of possible clipping the scene)
python inference.py --model results/release/semseg/final.ckpt --scenes METAROOT/sample/sample1/info.json --voxel_dim 208 208 80
Note that the values of voxel_dim must be divisible by 8 using the default 3D network.
Results will be saved to:
results/release/semseg/test_final/sample1.ply // mesh
results/release/semseg/test_final/sample1.npz // tsdf
results/release/semseg/test_final/sample1_attributes.npz // vertex semseg
To run on the entire Scannet test set use:
python inference.py --model results/release/semseg/final.ckpt
After running inference on Scannet you can run evaluation using:
python evaluate.py --model results/release/semseg/test_final/
Note that evaluate.py uses pyrender to render depth maps from the predicted mesh for 2D evaluation.
If you are using headless rendering you must also set the enviroment variable PYOPENGL_PLATFORM=osmesa
(see pyrender for more details).
You can print the results of a previous evaluation run using
python visualize_metrics.py --model results/release/semseg/test_final/
In addition to downloadinng and prepareing the data (as described above) you will also need to download our pretrained resnet50 weights (ported from detectron2) and unnzip it.
Then you can train your own models using train.py.
Configuration is controlled via a mix of config.yaml files and command line arguments.
We provide a few sample config files used in the paper in configs/.
Experiment names are specified by TRAINER.NAME and TRAINER.VERSION, which default to atlas and default.
See config.py for a full list of parameters.
python train.py --config configs/base.yaml TRAINER.NAME atlas TRAINER.VERSION base
python train.py --config configs/semseg.yaml TRAINER.NAME atlas TRAINER.VERSION semseg
To watch training progress use
tensorboard --logdir results/
We also provide scripts to run inference and evaluataion using COLMAP. Note that you must install COLMAP (which is included in our docker image).
For inference on the sample scene use
python inference_colmap.py --pathout results/colmap --scenes METAROOT/sample/sample1/info.json
and for Scannet
python inference_colmap.py --pathout results/colmap
To evaluate Scannet use
python evaluate_colmap.py --pathout results/colmap
@inproceedings{murez2020atlas,
title={Atlas: End-to-End 3D Scene Reconstruction from Posed Images},
author={Zak Murez and
Tarrence van As and
James Bartolozzi and
Ayan Sinha and
Vijay Badrinarayanan and
Andrew Rabinovich},
booktitle = {ECCV},
year = {2020},
url = {https://arxiv.org/abs/2003.10432}
}