Vincent Sitzmann*,
Semon Rezchikov*,
William Freeman,
Joshua Tenenbaum,
Frédo Durand
MIT, *denotes equal contribution
This is the official implementation of the paper "Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering".
In the branch "scene_rep_nets", we re-implemented our 2019 NeurIPS paper Scene Representation Networks, wich first proposed differentiable rendering of 3D neural fields using a sphere-tracing based renderer, enabling prior-based 3D reconstruction from only a single image observation. You can train both SRNs and LFNs using this codebase (all the instructions below also apply to SRNs)! Thanks to my MIT colleague Thomas O'Connell for helping with the SRNs re-implementation!
You can set up a conda environment with all dependencies like so:
conda env create -f environment.yml
conda activate lf
The code is organized as follows:
- multiclass_dataio.py and dataio.py contain the dataio for mutliclass- and single-class experiments respectively.
- models.py contains the code for light field networks.
- training.py contains a generic training routine.
- ./experiment_scripts/ contains scripts to reproduce experiments in the paper.
The directory experiment_scripts contains one script per experiment in the paper.
train_single_class.py trains a model on classes in the Scene Representation Networks format, such as cars or chairs. Note that since these datasets have a resolution of 128, this model starts with a lower resolution (64) and then increases the resolution to 128 (see line 43 in the script).
train_nmr.py trains a model on the NMR dataset. An example call is:
python experiment_scripts/train_nmr.py --data_root=path_to_nmr_dataset
python experiment_scripts/train_single_class.py --data_root=path_to_single_class
To reconstruct test objects, use the scripts "rec_single_class.py" and "rec_nmr.py". In addition to the data root, you have to point these scripts to the checkpoint from the training run. Note that the rec_nmr.py script uses the viewlist under ./experiment_scripts/viewlists/src_dvr.txt to pick which views to reconstruct the objects from, while rec_single_class.py per default reconstructs from the view with index 64.
python experiment_scripts/rec_nmr.py --data_root=path_to_nmr_dataset --checkpoint=path_to_training_checkpoint
python experiment_scripts/rec_single_class.py --data_root=path_to_single_class_TEST_SET --checkpoint=path_to_training_checkpoint
Finally, you may test the models on the test set with the test.py script. This script is used for testing all the models. You have to pass it as a parameter which dataset you are reconstructing ("NMR" or no). For the NMR dataset, you need to pass the "viewlist" again to make sure that the model is not evaluated on the context view.
python experiment_scripts/test.py --data_root=path_to_nmr_dataset --dataset=NMR --checkpoint=path_to_rec_checkpoint
python experiment_scripts/test.py --data_root=path_to_single_class_TEST_SET --dataset=single --checkpoint=path_to_rec_checkpoint
To monitor progress, both the training and reconstruction scripts write tensorboard summaries into a "summaries" subdirectory in the logging_root.
This code has a bunch of options that were not discussed in the paper.
- switch between a ReLU network and a SIREN to better fit high-frequency content with the flag --network (see the init of model.py for options).
- switch between a hypernetwork, conditioning via concatenation, and low-rank concditioning with the flag --conditioning
- there is an implementation of encoder-based inference in models.py (LFEncoder) which uses a ResNet18 with global conditioning to generate the latent codes z.
We use two types of datasets: class-specific ones and multi-class ones.
- The class-specific ones are loaded via dataio.py, and we provide them in a fast-to-load hdf5 format in our google drive.
- For the multiclass experiment, we use the ShapeNet 64x64 dataset from NMR https://s3.eu-central-1.amazonaws.com/avg-projects/differentiable_volumetric_rendering/data/NMR_Dataset.zip (Hosted by DVR authors). Additionally, you will need to place the intrinsics.txt file that you can find in the google drive in the nmr root directory.
This code uses an "OpenCV" style camera coordinate system, where the Y-axis points downwards (the up-vector points in the negative Y-direction), the X-axis points right, and the Z-axis points into the image plane. Camera poses are assumed to be in a "camera2world" format, i.e., they denote the matrix transform that transforms camera coordinates to world coordinates.
In my GitHub reading list on neural fields, I give an overview over prior work on neural fields & neural implicit representations.
In our CVPR 2018 paper DeepVoxels, we proposed a differentiable renderer
for overfitting a single voxel-grid based neural scene representations on a single scene, enabling photorealistic novel view synthesis
with high-frequency detail and view-dependent effects.
In our NeurIPS 2019 paper Scene Representation Networks, we proposed to leverage a 3D MLP / neural field / neural implicit based neural scene representation that could be trained via differentiable rendering.
In our NeurIPS 2021 paper Neural Implicit Representations with Periodic Activation Functions, we proposed periodic nonlinearities for neural fields / neural implicit representations that can both fit high-frequency signals, and can be used to parameterize signals with non-trivial higher-order derivatives.
If you find our work useful in your research, please cite:
@inproceedings{sitzmann2021lfns,
author = {Sitzmann, Vincent
and Rezchikov, Semon
and Freeman, William T.
and Tenenbaum, Joshua B.
and Durand, Fredo},
title = {Light Field Networks: Neural Scene Representations
with Single-Evaluation Rendering},
booktitle = {Proc. NeurIPS},
year={2021}
}
If you have any questions, please email Vincent Sitzmann at sitzmann@mit.edu.