We propose MVSplat, an efficient feed-forward 3D Gaussian Splatting model
learned from sparse multi-view images. To accurately localize the Gaussian
centers, we propose to build a cost volume representation via plane sweeping in
the 3D space, where the cross-view feature similarities stored in the cost
volume can provide valuable geometry cues to the estimation of depth. We learn
the Gaussian primitives' opacities, covariances, and spherical harmonics
coefficients jointly with the Gaussian centers while only relying on
photometric supervision. We demonstrate the importance of the cost volume
representation in learning feed-forward Gaussian Splatting models via extensive
experimental evaluations. On the large-scale RealEstate10K and ACID benchmarks,
our model achieves state-of-the-art performance with the fastest feed-forward
inference speed (22 fps). Compared to the latest state-of-the-art method
pixelSplat, our model uses $10\times $ fewer parameters and infers more than
$2\times$ faster while providing higher appearance and geometry quality as well
as better cross-dataset generalization.

我们提出了一种称为 MVSplat 的高效前馈 3D 高斯分割模型，通过稀疏多视图图像进行学习。为了准确定位高斯中心，我们通过在三维空间中进行平面扫描来建立成本体积表示，其中成本体积中存储的跨视图特征相似性可以为深度估计提供宝贵的几何线索。我们在只依靠光度监督的情况下，同时学习高斯原始图的透明度、协方差和球面谐波系数。通过广泛的实验评估，我们展示了成本体积表示在学习前馈高斯分割模型中的重要性。在大规模的 RealEstate10K 和 ACID 基准测试中，我们的模型以最快的前馈推断速度（22 帧 / 秒）实现了最先进的性能。与最新的最先进方法 pixelSplat 相比，我们的模型使用的参数数量少了 10 倍，推断速度更快了两倍，同时还提供更高的外观和几何质量以及更好的跨数据集泛化能力。