This is the training code for Radiance Meshes. The viewing code is here and the web viewer source code here.

Datasets: mipnerf360pt1, mipnerf360pt2, zipnerf

More details can be found on our website

Requirements

• NVIDIA GPU with 24GB or less of VRAM. Settings can be adjusted for lower amounts of VRAM. Reduce --log2_hashmap_size.
• Training code has only been tested on Linux. uv is known to have issues on Windows.

Install

Install UV here.

uv pip install setuptools
uv pip install torch

Dependencies: Then, it is as simple as running the command uv run instead of python to run the code. It installs everything super quickly. Example running command for bonsai:

PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True uv run train.py --eval  --dataset_path /data/nerf_datasets/360/bonsai --image_folder images_2 --output_path output/bonsai

Training for the web

To obtain a reduced model for mobile viewing, we recommend the following parameters:

• --within_thresh 4 Turns down the effective resolution of the densification
• --total_thresh 15 Turns down the effective resolution of the densification, but for thin structures
• --budget 250000 Limits the number of vertices to 250,000
• --voxel_size 0.1 Strongly reduces the number of initial vertices
• --iterations 10000 Train for a shorter amount of time
• --freeze_start 8000 Freeze earlier to reflect shorter training time
• --alpha_threshold 0 --density_threshold 0 For underwater scenes

Fisheye Lenses

Fisheye rendering is supported, fisheye training "works" but something seems a little wrong with the quality.

Ray tracing

Ray tracing is just a few steps away from being differentiable. Should speed up training.

Training on new Scenes

Put your set of images into a folder named $DATASET/input. Then run

python convert.py -s $DATASET --resize

Alternatively, you can use the Export for Gaussian Splatting option in Agisoft Metashape, as we use the same dataset format. Finally, you can train using the following command

PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True uv run train.py --dataset_path $DATASET --image_folder images_4 --output_path output/my_dataset

Benchmarking

To run our method on all datasets, you can edit the scripts/run_full.csv file to change the dataset paths, then run:

python hyperparam.py  --queue_csv scripts/run_full.csv --output_csv results/run_full_r46.csv --gpus 1,2,3 --suffix _r46

This is a general purpose script for running and collecting results across different parameters.