[ICCV 2025] LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos

Chin-Yang Lin · Cheng Sun · Fu-En Yang
Min-Hung Chen . Yen-Yu Lin · Yu-Lun Liu

Project Page | Paper | Evaluation Results

Overview

LongSplat is an unposed 3D Gaussian Splatting framework for robust reconstruction from casually captured long videos. Featuring incremental joint optimization, pose estimation with MASt3R, and adaptive octree anchoring, LongSplat achieves high-quality novel view synthesis from free viewpoints while remaining memory-efficient and scalable.

Installation

1. Clone LongSplat.

git clone --recursive https://github.com/NVlabs/LongSplat.git
cd LongSplat

2. Create the environment

conda create -n longsplat python=3.10.13 cmake=3.14.0 -y
conda activate longsplat
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia  # use the correct version of cuda for your system
pip install -r requirements.txt
pip install submodules/simple-knn
pip install submodules/diff-gaussian-rasterization
pip install submodules/fused-ssim

3. Optional but highly suggested, compile the cuda kernels for RoPE (as in CroCo v2).

# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.
cd submodules/mast3r/dust3r/croco/models/curope/
python setup.py build_ext --inplace
cd ../../../../../../

Dataset Preparation

DATAROOT is ./data by default. Please first make data folder by mkdir data.

Free Dataset

Download our preprocessed Free dataset from Dropbox, and save it into the ./data/free folder.

Hike Dataset

Download our preprocessed Hike dataset from Google Drive, and save it into the ./data/hike folder.

Tanks and Temples

Download the data preprocessed by Nope-NeRF as below, and the data is saved into the ./data/tanks folder.

wget https://www.robots.ox.ac.uk/~wenjing/Tanks.zip

Run

Training and Evaluation

The training scripts include both training, rendering, and evaluation steps:

Each .sh script runs three main Python scripts in sequence:

• train.py: Trains the LongSplat model
• render.py: Renders the trained model to generate novel views
• metrics.py: Evaluates the rendering quality and computes metrics

# For Free dataset
bash scripts/train_free.sh

# For Hike dataset
bash scripts/train_hike.sh

# For Tanks and Temples dataset
bash scripts/train_tnt.sh

Run on your own video

• To run LongSplat on your own video, you need to first convert your video to frames and save them to ./data/$CUSTOM_DATA/images/

• Before running the script, you need to modify the scene= parameter in scripts/train_custom.sh to point to your custom data directory. For example, change scene='./data/IMG_4190' to scene='./data/YOUR_CUSTOM_DATA'.

• Run the following command:

bash scripts/train_custom.sh

Acknowledgement

Our render is built upon 3DGS. The data processing and visualization codes are partially borrowed from Scaffold-GS. We thank all the authors for their great repos.

Citation

Please consider starring ⭐ this repo and citing our paper 📝 if you find it useful.

@inproceedings{lin2025longsplat,
  title={LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos},
  author={Chin-Yang Lin and Cheng Sun and Fu-En Yang and Min-Hung Chen and Yen-Yu Lin and Yu-Lun Liu},
  booktitle={ICCV},
  year={2025}
}