无需校准光源与传感器，实现动态表面法向恢复
Physics-Free Spectrally Multiplexed Photometric Stereo under Unknown Spectral Composition
- 不依赖物理模型，利用光谱混叠特性直接解码表面法向
- 首次构建SpectraM14数据集，支持无校准条件下的全面评估
- 适合复杂场景下快速部署的3D重建应用
本文提出一种革命性的光谱多路复用摄影测量方法，可在无需校准光源或传感器的条件下，恢复动态表面的法向信息，突破传统方法对严格先验和光谱模糊性的依赖。通过将光谱模糊性转化为优势，本方法无需专用多光谱渲染框架即可生成训练数据。我们设计了全新的无物理约束网络架构SpectraM-PS，能有效处理多路复用图像，在多种材质和条件下准确估计表面法向。同时，我们建立了首个针对光谱多路复用摄影测量的基准数据集SpectraM14，为现有校准方法提供全面评估基础。该工作显著提升了非校准环境下动态表面重建的能力，推动了摄影测量在多个领域的应用。
原文摘要 · Abstract (English)
In this paper, we present a groundbreaking spectrally multiplexed photometric stereo approach for recovering surface normals of dynamic surfaces without the need for calibrated lighting or sensors, a notable advancement in the field traditionally hindered by stringent prerequisites and spectral ambiguity. By embracing spectral ambiguity as an advantage, our technique enables the generation of training data without specialized multispectral rendering frameworks. We introduce a unique, physics-free network architecture, SpectraM-PS, that effectively processes multiplexed images to determine surface normals across a wide range of conditions and material types, without relying on specific physically-based knowledge. Additionally, we establish the first benchmark dataset, SpectraM14, for spectrally multiplexed photometric stereo, facilitating comprehensive evaluations against existing calibrated methods. Our contributions significantly enhance the capabilities for dynamic surface recovery, particularly in uncalibrated setups, marking a pivotal step forward in the application of photometric stereo across various domains.
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