自适应4D光照优化，用单相机同时高精度捕捉物体形状与反照率。
Differentiable Adaptive 4D Structured Illumination for Joint Capture of Shape and Reflectance
- 基于像素级概率模型，可微计算下一帧最优光照条件。
- 深度不确定性持续降低，最终重建精度优于当前主流方法。
- 适合需要精细材质与几何重建的3D扫描场景。
我们提出一种可微框架，针对物体自适应计算4D光照条件，实现高效、高质量的形状与反照率联合捕获，仅需统一的空间-角度结构光和单个相机。通过简单的直方图像素级概率模型，将下一帧光照条件与深度不确定性的减小目标进行可微关联。随着新结构光照入，对应图像测量值用于更新每个像素的不确定性。最后，采用微调策略，通过最小化所有物理测量值与其模拟结果之间的差异，重建深度图与反照率参数图。该框架在具有广泛形状和外观变化的真实物体上得到验证，深度结果优于现有最先进方法，反照率结果在与照片对比时表现相当。
原文摘要 · Abstract (English)
We present a differentiable framework to adaptively compute 4D illumination conditions with respect to an object, for efficient, high-quality simultaneous acquisition of its shape and reflectance, with a unified spatial-angular structured light and a single camera. Using a simple histogram-based pixel-level probability model for depth and reflectance, we differentiably link the next illumination condition(s) with a loss that encourages the reduction in depth uncertainty. As new structured illumination is cast, corresponding image measurements are used to update the uncertainty at each pixel. Finally, a fine-tuning-based approach reconstructs the depth map and reflectance parameter maps, by minimizing the differences between all physical measurements and their simulated counterparts. The effectiveness of our framework is demonstrated on physical objects with wide variations in shape and appearance. Our depth results compare favorably with state-of-the-art techniques, while our reflectance results are comparable when validated against photographs.
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