Neural approaches have shown a significant progress on camera-based
reconstruction. But they require either a fairly dense sampling of the viewing
sphere, or pre-training on an existing dataset, thereby limiting their
generalizability. In contrast, photometric stereo (PS) approaches have shown
great potential for achieving high-quality reconstruction under sparse
viewpoints. Yet, they are impractical because they typically require tedious
laboratory conditions, are restricted to dark rooms, and often multi-staged,
making them subject to accumulated errors. To address these shortcomings, we
propose an end-to-end uncalibrated multi-view PS framework for reconstructing
high-resolution shapes acquired from sparse viewpoints in a real-world
environment. We relax the dark room assumption, and allow a combination of
static ambient lighting and dynamic near LED lighting, thereby enabling easy
data capture outside the lab. Experimental validation confirms that it
outperforms existing baseline approaches in the regime of sparse viewpoints by
a large margin. This allows to bring high-accuracy 3D reconstruction from the
dark room to the real world, while maintaining a reasonable data capture
complexity.

通过提出一种端到端的非标定多视点 PS 框架，以实现在真实环境中通过稀疏视点获取高分辨率形状的重建，摒弃了黑暗室的假设，并允许静态环境光和动态 LED 光的组合，从而实现了在实验室外轻松捕获数据，并通过实验证实，在稀疏视点情况下超越了现有的基准方法，将高精度三维重建从黑暗室引入到现实世界中，并保持了合理的数据采集复杂性。

稀疏视角，近光源：非标定点照明光度立体的实用范式

Sparse Views, Near Light: A Practical Paradigm for Uncalibrated  Point-light Photometric Stereo

3D Gaussian Splatting has garnered extensive attention and application in
real-time neural rendering. Concurrently, concerns have been raised about the
limitations of this technology in aspects such as point cloud storage,
performance , and robustness in sparse viewpoints , leading to various
improvements. However, there has been a notable lack of attention to the
projection errors introduced by the local affine approximation inherent in the
splatting itself, and the consequential impact of these errors on the quality
of photo-realistic rendering. This paper addresses the projection error
function of 3D Gaussian Splatting, commencing with the residual error from the
first-order Taylor expansion of the projection function $\phi$. The analysis
establishes a correlation between the error and the Gaussian mean position.
Subsequently, leveraging function optimization theory, this paper analyzes the
function's minima to provide an optimal projection strategy for Gaussian
Splatting referred to Optimal Gaussian Splatting. Experimental validation
further confirms that this projection methodology reduces artifacts, resulting
in a more convincingly realistic rendering.

该研究论文探讨了 3D 高斯喷洒在图像渲染中的投影误差函数，进而提出了一种优化的投影策略，通过实验验证表明这种策略能够减少伪影，实现更真实的渲染效果。