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Neuralangelo

This is the official implementation of Neuralangelo: High-Fidelity Neural Surface Reconstruction.

Zhaoshuo Li, Thomas Müller, Alex Evans, Russell H. Taylor, Mathias Unberath, Ming-Yu Liu, Chen-Hsuan Lin
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023

Project page | Paper

The code is built upon the Imaginaire library from the Deep Imagination Research Group at NVIDIA.
For business inquiries, please submit the NVIDIA research licensing form.

Installation

We offer two ways to setup the environment:

  1. We provide prebuilt Docker images, where

    • docker.io/chenhsuanlin/colmap:3.8 is for running COLMAP and the data preprocessing scripts. This includes the prebuilt COLMAP library (CUDA-supported).
    • docker.io/chenhsuanlin/neuralangelo:23.04-py3 is for running the main Neuralangelo pipeline.

    The corresponding Dockerfiles can be found in the docker directory.

  2. The conda environment for Neuralangelo. Install the dependencies and activate the environment neuralangelo with

    conda env create --file neuralangelo.yaml
conda activate neuralangelo

For COLMAP, alternative installation options are also available on the COLMAP website.

Data preparation

Please refer to Data Preparation for step-by-step instructions.
We assume known camera poses for each extracted frame from the video. The code uses the same json format as Instant NGP.

Run Neuralangelo!

EXPERIMENT=toy_example
GROUP=example_group
NAME=example_name
CONFIG=projects/neuralangelo/configs/custom/${EXPERIMENT}.yaml
GPUS=1  # use >1 for multi-GPU training!
torchrun --nproc_per_node=${GPUS} train.py \
    --logdir=logs/${GROUP}/${NAME} \
    --config=${CONFIG} \
    --show_pbar

Some useful notes:

  • This codebase supports logging with Weights & Biases. You should have a W&B account for this.
    • Add --wandb to the command line argument to enable W&B logging.
    • Add --wandb_name to specify the W&B project name.
    • More detailed control can be found in the init_wandb() function in imaginaire/trainers/base.py.
  • Configs can be overridden through the command line (e.g. --optim.params.lr=1e-2).
  • Set --checkpoint={CHECKPOINT_PATH} to initialize with a certain checkpoint; set --resume to resume training.
  • If appearance embeddings are enabled, make sure data.num_images is set to the number of training images.

Isosurface extraction

Use the following command to run isosurface mesh extraction:

CHECKPOINT=logs/${GROUP}/${NAME}/xxx.pt
OUTPUT_MESH=xxx.ply
CONFIG=projects/neuralangelo/configs/custom/${EXPERIMENT}.yaml
RESOLUTION=2048
BLOCK_RES=128
GPUS=1  # use >1 for multi-GPU mesh extraction
torchrun --nproc_per_node=${GPUS} projects/neuralangelo/scripts/extract_mesh.py \
    --logdir=logs/${GROUP}/${NAME} \
    --config=${CONFIG} \
    --checkpoint=${CHECKPOINT} \
    --output_file=${OUTPUT_MESH} \
    --resolution=${RESOLUTION} \
    --block_res=${BLOCK_RES}

Some useful notes:

  • Add --textured to extract meshes with textures.
  • Lower BLOCK_RES to reduce GPU memory usage.
  • Lower RESOLUTION to reduce mesh size.

Citation

If you find our code useful for your research, please cite

@inproceedings{li2023neuralangelo,
  title={Neuralangelo: High-Fidelity Neural Surface Reconstruction},
  author={Li, Zhaoshuo and M\"uller, Thomas and Evans, Alex and Taylor, Russell H and Unberath, Mathias and Liu, Ming-Yu and Lin, Chen-Hsuan},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition ({CVPR})},
  year={2023}
}