  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.
