MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images

Codes for the following paper:

MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images
Benjamin Attal, Selena Ling, Aaron Gokaslan, Christian Richardt, James Tompkin
ECCV 2020

See more at our project page.

If you use these codes, please cite:

@inproceedings{Attal:2020:ECCV,
    author    = "Benjamin Attal and Selena Ling and Aaron Gokaslan and Christian Richardt and James Tompkin",
    title     = "{MatryODShka}: Real-time {6DoF} Video View Synthesis using Multi-Sphere Images",
    booktitle = "European Conference on Computer Vision (ECCV)",
    month     = aug,
    year      = "2020",
    url       = "https://visual.cs.brown.edu/matryodshka"
}

Note that our codes are based on the code from the paper "Stereo Maginification: Learning View Synthesis using Multiplane Images" by Zhou et al. [1], and on the code from the paper "Pixel2mesh: Generating 3D Mesh Models from Single RGB Images." by Wang et al. [3]. Please also cite their work.

Setup

• Create a conda environment from the matryodshka-gpu.yml file.
• Run ./download_glob.sh to download the files needed for training and testing.
• Download the dataset as in Section Replica dataset.

Training the model

See train.py for training the model.

• To train with transform inverse regularization, use --transform_inverse_reg flag.

• To train with CoordNet, use --coord_net flag.

• To experiment with different losses (elpips or l2), use --which_loss flag.

  • To train with spherical weighting on loss maps, use --spherical_attention flag.

• To train with graph convolution network (GCN), use --gcn flag. Note the particular GCN architecture definition we used is from the Pixel2Mesh repo [3].

• The current scripts support training on Replica 360 and cubemap dataset and RealEstate10K dataset. Use --input_type to switch between these types of inputs (ODS, PP, REALESTATE_PP).

See scripts/train/*.sh for some sample scripts.

Testing the model

See test.py for testing the model with replica-360 test set.

• When testing on video frames, e.g. test_video_640x320, include on_video in --test_type flag.
• When testing on high-resolution images, include high_res in --test_type flag.

See scripts/test/*.sh for sample scripts.

Evaluation

See eval.py for evaluating the model, which saves the metric scores into a json file. We evaluate our models on

• third-view reconstruction quality

  • See scripts/eval/*-reg.sh for a sample script.

• frame-to-frame reconstruction differences on video sequences to evaluate the effect of transform inverse regularization on temporal consistency.

  • Include on_video when specifying the --eval_type flag.
  • See scripts/eval/*-video.sh for a sample script.

Pre-trained model

Download models pre-trained with and without transform inverse regularization by running ./download_model.sh. These can also be found here at the Brown library for archival purposes.

Replica dataset

We rendered a 360 and a cubemap dataset for training from the Facebook Replica Dataset [2]. This data can be found here at the Brown library for archival purposes. You should have access to the following datasets.

• train_640x320
• test_640x320
• test_video_640x320

You can also find the camera pose information here that were used to render the training dataset. Each line of the txt fileach line of the txt file is formatted as below:

camera_position_x camera_position_y camera_position_z ods_baseline target1_offset_x target1_offset_y target1_offset_z target2_offset_x target2_offset_y target2_offset_z target3_offset_x target3_offset_y target3_offset_z

We also have a fork of the Replica dataset codebase which can regenerate our data from scratch. This contains customized rendering scripts that allow output of ODS, equirectangular, and cubemap projection spherical imagery, along with corresponding depth maps.

Note that the 360 dataset we release for download was rendered with an incorrect 90-degree camera rotation around the up vector and a horizontal flip. Regenerating the dataset from our released code fork with the customized rendering scripts will not include this coordinate change. The output model performance should be approximately the same.

Exporting the model to ONNX

We export our model to ONNX by firstly converting the checkpoint into a pb file, which then gets converted to an onnx file with the tf2onnx module. See export.py for exporting the model into .pb file.

See scripts/export/model-name.sh for a sample script to run export.py, and scripts/export/pb2onnx.sh for a sample script to run pb-to-onnx conversion.

Unity Application + ONNX to TensorRT Conversion

We are still working on releasing the real-time Unity application and onnx2trt conversion scripts. Please bear with us!

References

[1] Zhou, Tinghui, et al. "Stereo magnification: Learning view synthesis using multiplane images." arXiv preprint arXiv:1805.09817 (2018). https://github.com/google/stereo-magnification

[2] Straub, Julian, et al. "The Replica dataset: A digital replica of indoor spaces." arXiv preprint arXiv:1906.05797 (2019). https://github.com/facebookresearch/Replica-Dataset

[3] Wang, Nanyang, et al. "Pixel2mesh: Generating 3d mesh models from single rgb images." Proceedings of the European Conference on Computer Vision (ECCV). 2018. https://github.com/nywang16/Pixel2Mesh