LaRa: Efficient Large-Baseline Radiance Fields

Project page | Paper | Data | Checkpoint |

⭐ New Features

2024/04/05: Important updates - Now our method supports half precision training, achieving over 100% faster convergence and about 1.5dB gains with less iterations!

Model PSNR ↑ SSIM ↑ Abs err (Geo) ↓ Epoch Time（day） ckpt

Paper 27.65 0.951 0.0654 50 3.5 ------

bf16 29.15 0.956 0.0574 30 1.5 Download

Please download the pre-trained checkpoint from the provided link and place it in the ckpts folder.

Model	PSNR ↑	SSIM ↑	Abs err (Geo) ↓	Epoch	Time（day）	ckpt
Paper	27.65	0.951	0.0654	50	3.5	------
bf16	29.15	0.956	0.0574	30	1.5	Download

Installation

git clone https://github.com/autonomousvision/LaRa.git --recursive
conda env create --file environment.yml
conda activate lara

Dataset

We used the processed gobjaverse dataset for training. A download script tools/download_dataset.py is provided to automatically download the datasets.

python tools/download_dataset.py all

Note: The GObjaverse dataset requires approximately 1.4 TB of storage. You can also download a subset of the dataset. Please refer to the provided script for details. Please manually delete the _temp folder after completing the download.

If you would like to process the data by yourself, we provide preprocess scripts for the gobjaverse and co3d datasets, please check tools/prepare_dataset_*. You can also download our preprocessed data and put them to dataset folder:

gobjaverse
Google Scaned Object
Co3D
Instant3D - Please contact the authors of Instant3D if you wish to obtain the data for comparison.

Training

python train_lightning.py

note: You can configure the GPU id and other parameter with configs/base.yaml.

Evaluation

Our method supports the reconstruction of radiance fields from multi-view, text, and single view inputs. We provide a pre-trained checkpoint at ckpt.

multi-view to 3D

To reproduce the table results, you can simply use:

python eval_all.py

note:

Please double-check that the paths inside the script are correct for your specific case.
Please specify the video_frames and save_mesh labels if you would like to output mesh or video during the evaluation

text to 3D

python evaluation.py configs/infer.yaml 
       infer.ckpt_path=ckpts/epoch=29.ckpt
       infer.save_folder=outputs/prompts/
       infer.dataset.generator_type=xxx
       infer.dataset.prompts=["a car made out of sushi","a beautiful rainbow fish"]

note: This part is currently unavailable due to a permissions issue. I will look for an alternative text-to-multi-view generator later next week.

single view to 3D

python evaluation.py configs/infer.yaml 
       infer.ckpt_path=ckpts/epoch=29.ckpt
       infer.save_folder=outputs/single-view/
       infer.dataset.generator_type="zero123plus-v1"
       infer.dataset.image_pathes=\["assets/examples/13_realfusion_cherry_1.png"\]

note: It supports the generator types zero123plus-v1.1 and zero123plus-v1.

Acknowledgements

Our render is built upon 2DGS. The data preprocessing code for the Co3D dataset is partially borrowed from Splatter-Image. Additionally, the script for generating multi-view images from text and single view image is sourced from GRM. We thank all the authors for their great repos.

Citation

If you find our code or paper helps, please consider citing:

@inproceedings{LaRa,
         author = {Anpei Chen and Haofei Xu and Stefano Esposito and Siyu Tang and Andreas Geiger},
         title = {LaRa: Efficient Large-Baseline Radiance Fields},
         booktitle = {European Conference on Computer Vision (ECCV)},
         year = {2024}
        }