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base on 🌊 [ECCV'24 Oral] MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images <p align="center"> <h1 align="center">MVSplat: Efficient 3D Gaussian Splatting <br> from Sparse Multi-View Images</h1> <p align="center"> <a href="https://donydchen.github.io/">Yuedong Chen</a>  ·  <a href="https://haofeixu.github.io/">Haofei Xu</a>  ·  <a href="https://chuanxiaz.com/">Chuanxia Zheng</a>  ·  <a href="https://bohanzhuang.github.io/">Bohan Zhuang</a> <br> <a href="https://people.inf.ethz.ch/marc.pollefeys/">Marc Pollefeys</a>  ·  <a href="http://www.cvlibs.net/">Andreas Geiger</a>  ·  <a href="https://personal.ntu.edu.sg/astjcham/">Tat-Jen Cham</a>  ·  <a href="https://jianfei-cai.github.io/">Jianfei Cai</a> </p> <h3 align="center">ECCV 2024 Oral</h3> <h3 align="center"><a href="https://arxiv.org/abs/2403.14627">Paper</a> | <a href="https://donydchen.github.io/mvsplat/">Project Page</a> | <a href="https://drive.google.com/drive/folders/14_E_5R6ojOWnLSrSVLVEMHnTiKsfddjU">Pretrained Models</a> </h3>  <ul> <li><b>20/01/25 Update:</b> Check out Cheng's <a href="https://github.com/chengzhag/PanSplat">PanSplat [CVPR '25]</a>, which extends MVSplat to higher resolutions (up to 4K) and highlights the use of a hierarchical spherical cost volume and two-step deferred backpropagation for memory-efficient training. </li> <li><b>08/11/24 Update:</b> Explore our <a href="https://github.com/donydchen/mvsplat360">MVSplat360 [NeurIPS '24]</a>, an upgraded MVSplat that combines video diffusion to achieve 360° NVS for large-scale scenes from just 5 input views! </li> <li><b>21/10/24 Update:</b> Check out Haofei's <a href="https://github.com/cvg/depthsplat">DepthSplat [CVPR '25]</a> if you are interested in feed-forward 3DGS on more complex scenes (DL3DV-10K) and more input views (up to 12 views)!</li> </ul> <br> </p> https://github.com/donydchen/mvsplat/assets/5866866/c5dc5de1-819e-462f-85a2-815e239d8ff2 ## Installation To get started, clone this project, create a conda virtual environment using Python 3.10+, and install the requirements: ```bash git clone https://github.com/donydchen/mvsplat.git cd mvsplat conda create -n mvsplat python=3.10 conda activate mvsplat pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118 pip install -r requirements.txt ``` ## Acquiring Datasets ### RealEstate10K and ACID Our MVSplat uses the same training datasets as pixelSplat. Below we quote pixelSplat's [detailed instructions](https://github.com/dcharatan/pixelsplat?tab=readme-ov-file#acquiring-datasets) on getting datasets. > pixelSplat was trained using versions of the RealEstate10k and ACID datasets that were split into ~100 MB chunks for use on server cluster file systems. Small subsets of the Real Estate 10k and ACID datasets in this format can be found [here](https://drive.google.com/drive/folders/1joiezNCyQK2BvWMnfwHJpm2V77c7iYGe?usp=sharing). To use them, simply unzip them into a newly created `datasets` folder in the project root directory. > If you would like to convert downloaded versions of the Real Estate 10k and ACID datasets to our format, you can use the [scripts here](https://github.com/dcharatan/real_estate_10k_tools). Our preprocessed versions of the datasets can be found [here](http://schadenfreude.csail.mit.edu:8000/). ### DTU (For Testing Only) * Download the preprocessed DTU data [dtu_training.rar](https://drive.google.com/file/d/1eDjh-_bxKKnEuz5h-HXS7EDJn59clx6V/view). * Convert DTU to chunks by running `python src/scripts/convert_dtu.py --input_dir PATH_TO_DTU --output_dir datasets/dtu` * [Optional] Generate the evaluation index by running `python src/scripts/generate_dtu_evaluation_index.py --n_contexts=N`, where N is the number of context views. (For N=2 and N=3, we have already provided our tested version under `/assets`.) ## Running the Code ### Evaluation To render novel views and compute evaluation metrics from a pretrained model, * get the [pretrained models](https://drive.google.com/drive/folders/14_E_5R6ojOWnLSrSVLVEMHnTiKsfddjU), and save them to `/checkpoints` * run the following: ```bash # re10k python -m src.main +experiment=re10k \ checkpointing.load=checkpoints/re10k.ckpt \ mode=test \ dataset/view_sampler=evaluation \ test.compute_scores=true # acid python -m src.main +experiment=acid \ checkpointing.load=checkpoints/acid.ckpt \ mode=test \ dataset/view_sampler=evaluation \ dataset.view_sampler.index_path=assets/evaluation_index_acid.json \ test.compute_scores=true ``` * the rendered novel views will be stored under `outputs/test` To render videos from a pretrained model, run the following ```bash # re10k python -m src.main +experiment=re10k \ checkpointing.load=checkpoints/re10k.ckpt \ mode=test \ dataset/view_sampler=evaluation \ dataset.view_sampler.index_path=assets/evaluation_index_re10k_video.json \ test.save_video=true \ test.save_image=false \ test.compute_scores=false ``` ### Training Run the following: ```bash # download the backbone pretrained weight from unimatch and save to 'checkpoints/' wget 'https://s3.eu-central-1.amazonaws.com/avg-projects/unimatch/pretrained/gmdepth-scale1-resumeflowthings-scannet-5d9d7964.pth' -P checkpoints # train mvsplat python -m src.main +experiment=re10k data_loader.train.batch_size=14 ``` Our models are trained with a single A100 (80GB) GPU. They can also be trained on multiple GPUs with smaller RAM by setting a smaller `data_loader.train.batch_size` per GPU. <details> <summary><b>Training on multiple nodes (https://github.com/donydchen/mvsplat/issues/32)</b></summary> Since this project is built on top of pytorch_lightning, it can be trained on multiple nodes hosted on the SLURM cluster. For example, to train on 2 nodes (with 2 GPUs on each node), add the following lines to the SLURM job script ```bash #SBATCH --nodes=2 # should match with trainer.num_nodes #SBATCH --gres=gpu:2 # gpu per node #SBATCH --ntasks-per-node=2 # optional, for debugging export NCCL_DEBUG=INFO export HYDRA_FULL_ERROR=1 # optional, set network interface, obtained from ifconfig export NCCL_SOCKET_IFNAME=[YOUR NETWORK INTERFACE] # optional, set IB GID index export NCCL_IB_GID_INDEX=3 # run the command with 'srun' srun python -m src.main +experiment=re10k \ data_loader.train.batch_size=4 \ trainer.num_nodes=2 ``` References: * [Pytorch Lightning: RUN ON AN ON-PREM CLUSTER (ADVANCED)](https://lightning.ai/docs/pytorch/stable/clouds/cluster_advanced.html) * [NCCL: How to set NCCL_SOCKET_IFNAME](https://github.com/NVIDIA/nccl/issues/286) * [NCCL: NCCL WARN NET/IB](https://github.com/NVIDIA/nccl/issues/426) </details> <details> <summary><b>Fine-tune from the released weights (https://github.com/donydchen/mvsplat/issues/45)</b></summary> To fine-tune from the released weights <i>without</i> loading the optimizer states, run the following: ```bash python -m src.main +experiment=re10k data_loader.train.batch_size=14 \ checkpointing.load=checkpoints/re10k.ckpt \ checkpointing.resume=false ``` </details> ### Ablations We also provide a collection of our [ablation models](https://drive.google.com/drive/folders/14_E_5R6ojOWnLSrSVLVEMHnTiKsfddjU) (under folder 'ablations'). To evaluate them, *e.g.*, the 'base' model, run the following command ```bash # Table 3: base python -m src.main +experiment=re10k \ checkpointing.load=checkpoints/ablations/re10k_worefine.ckpt \ mode=test \ dataset/view_sampler=evaluation \ test.compute_scores=true \ wandb.name=abl/re10k_base \ model.encoder.wo_depth_refine=true ``` ### Cross-Dataset Generalization We use the default model trained on RealEstate10K to conduct cross-dataset evaluations. To evaluate them, *e.g.*, on DTU, run the following command ```bash # Table 2: RealEstate10K -> DTU python -m src.main +experiment=dtu \ checkpointing.load=checkpoints/re10k.ckpt \ mode=test \ dataset/view_sampler=evaluation \ dataset.view_sampler.index_path=assets/evaluation_index_dtu_nctx2.json \ test.compute_scores=true ``` **More running commands can be found at [more_commands.sh](more_commands.sh).** ## BibTeX ```bibtex @article{chen2024mvsplat, title = {MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images}, author = {Chen, Yuedong and Xu, Haofei and Zheng, Chuanxia and Zhuang, Bohan and Pollefeys, Marc and Geiger, Andreas and Cham, Tat-Jen and Cai, Jianfei}, journal = {arXiv preprint arXiv:2403.14627}, year = {2024}, } ``` ## Acknowledgements The project is largely based on [pixelSplat](https://github.com/dcharatan/pixelsplat) and has incorporated numerous code snippets from [UniMatch](https://github.com/autonomousvision/unimatch). Many thanks to these two projects for their excellent contributions! 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