Eldar Insafutdinov*, Dylan Campbell*, Joao F Henriques and Andrea Vedaldi. ECCV 2022

Paper | Project page

Create a conda environment with python 3.9, pytorch 1.11 for CUDA 11.3 and pytorch3d 0.6.2:

conda env create -f environment.yml

Download a CO3D car category (or others) here and extract to data/ such that it is organised like so:

data/co3d
|-- car
    |-- <scene_id>
        |-- images
        |-- masks
        |-- depths
        |-- depth_masks
    ...
|-- toyplane
...

Extract 3D bounding box fits for selected categories:

cd data; tar xzvf co3d_extra_data.tar.gz; cd ..

Run the following command to train the model on a single scene using the structured train/val split as described in the paper:

(EXP=car/structured ID=157_17286_33548; python exp_runner.py gpu=0 mode=train config.file=exp/$EXP/config.yaml config.exp_name=$EXP/${ID} dataset.instance=\'$ID\')

Training logs and model checkpoints of this run will be saved under exp/car/structured/157_17286_33548. In order to reproduce the results in the paper repeat the command above for every scene listed in lists/co3d_car_structured_split.txt.

Similarly, to train a model on the official CO3D split replace car/structured with car/official in the command above.

Render test views:

python scripts/render_nvs_predictions.py --gpu=0 --exp=car/structured --instance=157_17286_33548

Evaluate:

python scripts/evaluate.py --gpu=0 --exp=car/structured

First run the following script:

python exp_runner.py gpu=0 mode=visualise_mesh test.web_vis=true config.exp_name=car/structured/157_17286_33548 visualisation.port=8888

And then open the link in the browser http://localhost:8888.

Use the notebook scripts/vis_epipolar.ipynb for interactive epipolar line visualisation. Click on anywhere in the left image and the corresponding epipolar line on the right will be shown

Use scripts/vis_cameras.py (script adapted from NeRF++) to visualise cameras.

If you find this work useful consider citing our paper:

@article{insafutdinov2022snes,
  title={SNeS: Learning Probably Symmetric Neural Surfaces from Incomplete Data},
  author={Insafutdinov, Eldar and Campbell, Dylan and Henriques, Jo{\~a}o F and Vedaldi, Andrea},
  journal={arXiv preprint arXiv:2206.06340},
  year={2022}
}

The starting point for this project was the implementation of NeuS. We thank the authors of this excellent paper.