Skip to content

magicleap/Atlas

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

ATLAS: End-to-End 3D Scene Reconstruction from Posed Images

Project Page | Paper | Video | Models | Sample Data

Zak Murez, Tarrence van As, James Bartolozzi, Ayan Sinha, Vijay Badrinarayanan, and Andrew Rabinovich

Quickstart

We provide a Colab Notebook to try inference.

Installation

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).

Data Preperation

Sample

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

Scannet

Download and extract Scannet by following the instructions provided at http://www.scan-net.org/. You also need to download the train/val/test splits and the label mapping from https://github.com/ScanNet/ScanNet (Benchmark Tasks). The directory structure should look like:

DATAROOT
└───scannet
│   └───scans
│   |   └───scene0000_00
│   |       └───color
│   |       │   │   0.jpg
│   |       │   │   1.jpg
│   |       │   │   ...
│   |       │   ...
│   └───scans_test
│   |       └───color
│   |       │   │   0.jpg
│   |       │   │   1.jpg
│   |       │   │   ...
│   |       │   ...
|   └───scannetv2-labels.combined.tsv
|   └───scannetv2_test.txt
|   └───scannetv2_train.txt
|   └───scannetv2_val.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). 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.

Your own data

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).

Inference

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

Evaluation

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/

Training

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/

COLMAP Baseline

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

Citation

@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}
}