Sparse Multi-view Images can be Learned to predict explicit radiance fields via Generalizable Gaussian Splatting approaches, which can achieve wider application prospects in real-life when ground-truth camera parameters are not required as inputs. In this paper, a novel generalizable Gaussian Splatting method, SmileSplat, is proposed to reconstruct pixel-aligned Gaussian surfels for diverse scenarios only requiring unconstrained sparse multi-view images. First, Gaussian surfels are predicted based on the multi-head Gaussian regression decoder, which can are represented with less degree-of-freedom but have better multi-view consistency. Furthermore, the normal vectors of Gaussian surfel are enhanced based on high-quality of normal priors. Second, the Gaussians and camera parameters (both extrinsic and intrinsic) are optimized to obtain high-quality Gaussian radiance fields for novel view synthesis tasks based on the proposed Bundle-Adjusting Gaussian Splatting module. Extensive experiments on novel view rendering and depth map prediction tasks are conducted on public datasets, demonstrating that the proposed method achieves state-of-the-art performance in various 3D vision tasks.
稀疏多视角图像可以通过可泛化的高斯投影方法预测显式辐射场，从而在不需要真实相机参数作为输入的情况下实现更广泛的现实应用。本文提出了一种新颖的可泛化高斯投影方法 SmileSplat，仅依赖无约束的稀疏多视角图像即可在多样化场景中重建像素对齐的高斯表面元（surfels）。 首先，SmileSplat 基于多头高斯回归解码器预测高斯表面元，具有较少的自由度表示，同时实现更好的多视角一致性。此外，通过高质量的法线先验增强了高斯表面元的法线向量精度。其次，我们设计了一个 Bundle-Adjusting Gaussian Splatting 模块，优化高斯和相机参数（包括外参和内参），以生成高质量的高斯辐射场用于新视角合成任务。 在公共数据集上的新视角渲染和深度图预测任务的广泛实验表明，所提出的方法在多种 3D 视觉任务中实现了最先进的性能，展示了其在真实应用中的潜力和高效性。