用高斯点阵与逆渲染重建三维场景光照，更直观高效。
Photometric Stereo using Gaussian Splatting and inverse rendering
- 基于高斯点阵参数化三维场景，优化过程更可解释。
- 简化光照建模，实现高质量的光度立体重建。
- 适合关注三维重建与渲染的新方法研究者。
当前光度立体领域的最先进算法依赖神经网络，通过先验学习或逆渲染优化实现。本文重新审视校准光度立体问题，利用高斯点阵形式下的3D逆渲染最新进展。该方法能够参数化待重建的3D场景，并以更具可解释性的方式进行优化。所提出的方法采用简化的光照表示模型，验证了高斯点阵渲染引擎在光度立体任务中的潜力。
原文摘要 · Abstract (English)
Recent state-of-the-art algorithms in photometric stereo rely on neural networks and operate either through prior learning or inverse rendering optimization. Here, we revisit the problem of calibrated photometric stereo by leveraging recent advances in 3D inverse rendering using the Gaussian Splatting formalism. This allows us to parameterize the 3D scene to be reconstructed and optimize it in a more interpretable manner. Our approach incorporates a simplified model for light representation and demonstrates the potential of the Gaussian Splatting rendering engine for the photometric stereo problem.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。