MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo
<|reference_start|>MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo: We present MVSGaussian, a new generalizable 3D Gaussian representation approach derived from Multi-View Stereo (MVS) that can efficiently reconstruct unseen scenes. Specifically, 1) we leverage MVS to encode geometry-aware Gaussian representations and decode them into Gaussian parameters. 2) To further enhance performance, we propose a hybrid Gaussian rendering that integrates an efficient volume rendering design for novel view synthesis. 3) To support fast fine-tuning for specific scenes, we introduce a multi-view geometric consistent aggregation strategy to effectively aggregate the point clouds generated by the generalizable model, serving as the initialization for per-scene optimization. Compared with previous generalizable NeRF-based methods, which typically require minutes of fine-tuning and seconds of rendering per image, MVSGaussian achieves real-time rendering with better synthesis quality for each scene. Compared with the vanilla 3D-GS, MVSGaussian achieves better view synthesis with less training computational cost. Extensive experiments on DTU, Real Forward-facing, NeRF Synthetic, and Tanks and Temples datasets validate that MVSGaussian attains state-of-the-art performance with convincing generalizability, real-time rendering speed, and fast per-scene optimization.<|reference_end|>
@article{liu2024mvsgaussian:,
    title={MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from
  Multi-View Stereo},
    author={Tianqi Liu, Guangcong Wang, Shoukang Hu, Liao Shen, Xinyi Ye, Yuhang
  Zang, Zhiguo Cao, Wei Li, Ziwei Liu},
    journal={arXiv preprint arXiv:2405.12218},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12218},
    primaryClass={cs.CV}
}