PyTorch implementation of the ICCV 2023 paper.
Tiled Multiplane Images for Practical 3D Photography
Numair Khan1, Douglas Lanman1, Lei Xiao1
1Reality Labs Research
The code has been tested in the following setup
- Linux (Ubuntu 20.04.04/Fedora 34)
- Python 3.7
- PyTorch 1.10.2
- CUDA 11.3
We recommend running the code in a Conda environment: after cloning the repo, run the following commands from the base directory:
$ conda env create -f environment.yml
$ conda activate tmpi
Run the initialization script to download model checkpoints and set up the monocular depth estimator; we use DPT:
$ sh ./initialize.sh
To execute 3D photos from the test images provided in the test_data folder run:
$ python ./run.py
The results are written to the output folder.
To execute the code on your own images use:
$ python ./run.py --indir=/PATH_TO_INPUT_IMAGES_DIR --outdir=/PATH_TO_OUTPUT_DIR
By default, the code uses OpenGL to render the tiled multiplane images efficiently. We notice that on some implementations this may cause flickering around a small number of tile edges. To avoid this (or in case your system does not have OpenGL installed) the differentiable PyTorch renderer we use for training may be utilized by providing the --pytorch_renderer flag.
$ python ./run.py --pytorch_renderer --indir=/PATH_TO_INPUT_IMAGES_DIR --outdir=/PATH_TO_OUTPUT_DIR
Note, however, that this will run much slower.
While we use DPT as the depth estimator, the method can work with any depth input. To use a different source, we suggest replacing the DPT depth loader on L87 of the dataset.py file with the desired (inverse) depth input.
If you find our work useful for your research, please cite the following paper:
@article{khan2023tcod,
title={Tiled Multiplane Images for Practical 3D Photography},
author={Numair Khan, Eric Penner, Douglas Lanman, Lei Xiao},
journal={International Conference on Computer Vision (ICCV)},
year={2023},
}
Our source code is CC-BY-NC licensed, as found in the LICENSE file.