Fast Training of Neural Lumigraph Representations using Meta Learning

Project Page | Paper | Data

Alexander W. Bergman, Petr Kellnhofer, Gordon Wetzstein, Stanford University.
Official Implementation for Fast Training of Neural Lumigraph Representations using Meta Learning.

Usage

To get started, create a conda environment with all dependencies:

conda env create -f environment.yml
conda activate metanlrpp

Code Structure

The code is organized as follows:

• experiment_scripts: directory containing scripts to for training and testing MetaNLR++ models.
  • pretrain_features.py: pre-train encoder and decoder networks
  • train_sdf_ibr_meta.py: train meta-learned initialization for encoder, decoder, aggregation fn, and neural SDF
  • test_sdf_ibr_meta.py: specialize meta-learned initialization to a specific scene
  • train_sdf_ibr.py: train NLR++ model from scratch without meta-learned initialization
  • test_sdf_ibr.py: evaluate performance on withheld views
• configs: directory containing configs to reproduce experiments in the paper
  • nlrpp_nlr.txt: configuration for training NLR++ on the NLR dataset
  • nlrpp_dtu.txt: configuration for training NLR++ on the DTU dataset
  • nlrpp_nlr_meta.txt: configuration for training the MetaNLR++ initialization on the NLR dataset
  • nlrpp_dtu_meta.txt: configuration for training the MetaNLR++ initialization on the DTU dataset
  • nlrpp_nlr_metaspec.txt: configuration for training MetaNLR++ on the NLR dataset using the learned initialization
  • nlrpp_dtu_metaspec.txt: configuration for training MetaNLR++ on the DTU dataset using the learned initialization
• data_processing: directory containing utility functions for processing data
• torchmeta: torchmeta library for meta-learning
• utils: directory containing various utility functions for rendering and visualization
• loss_functions.py: file containing loss functions for evaluation
• meta_modules.py: contains meta learning wrappers around standard modules using torchmeta
• modules.py: contains standard modules for coodinate-based networks
• modules_sdf.py: extends standard modules for coordinate-based network representations of signed-distance functions.
• modules_unet.py: contains encoder and decoder modules used for image-space feature processing
• scheduler.py: utilities for training schedule
• training.py: training script
• sdf_rendering.py: functions for rendering SDF
• sdf_meshing.py: functions for meshing SDF
• checkpoints: contains checkpoints to some pre-trained models (additional/ablation models by request)
• assets: contains paths to checkpoints which are used as assets, and pre-computed buffers over multiple runs (if necessary)

Getting Started

Pre-training Encoder and Decoder

Pre-train the encoder and decoder using the FlyingChairsV2 training dataset as follows:

python experiment_scripts/pretrain_features.py --experiment_name XXX --batch_size X --dataset_path /path/to/FlyingChairs2/train

Alternatively, use the checkpoint in the checkpoints directory.

Training NLR++

Train a NLR++ model using the following command:

python experiment_scripts/train_sdf_ibr.py --config_filepath configs/nlrpp_dtu.txt --experiment_name XXX --dataset_path /path/to/dtu/scanXXX --checkpoint_img_encoder /path/to/pretrained/encdec

Note that we have uploaded our processed version of the DTU and NLR data here. The raw NLR data can be found here.

Meta-learned Initialization (MetaNLR++)

Meta-learn the initialization for the encoder, decoder, aggregation function, and neural SDF using the following command:

python experiment_scripts/train_sdf_ibr_meta.py --config_filepath configs/nlrpp_dtu_meta.txt --experiment_name XXX --dataset_path /path/to/dtu/meta/training --reference_view 24 --checkpoint_img_encoder /path/to/pretrained/encdec

Some optimized initializations for the DTU and NLR datasets can be found in the data directory. Additional models can be provided upon request.

Training MetaNLR++ from Initialization

Use the meta-learned initialization to specialize to a specific scene using the following command:

python experiment_scripts/test_sdf_ibr_meta.py --config_filepath configs/nlrpp_dtu_metaspec.txt --experiment_name XXX --dataset_path /path/to/dtu/scanXXX --reference_view 24 --meta_initialization /path/to/learned/meta/initialization

Evaluation

Test the converged scene on withheld views using the following command:

python experiment_scripts/test_sdf_ibr.py --config_filepath configs/nlrpp_dtu.txt --experiment_name XXX --dataset_path /path/to/dtu/scanXXX --checkpoint_path_test /path/to/checkpoint/to/evaluate

Citation \& Contact

If you find our work useful in your research, please cite

@inproceedings{bergman2021metanlr,
author = {Bergman, Alexander W. and Kellnhofer, Petr and Wetzstein, Gordon},
title = {Fast Training of Neural Lumigraph Representations using Meta Learning},
booktitle = {NeurIPS},
year = {2021},
}

If you have any questions or would like access to specific ablations or baselines presented in the paper or supplement (the code presented here is only a subset based off of the source code used to generate the results), please feel free to contact the authors. Alex can be contacted via e-mail at awb@stanford.edu.