面向形状与反射率联合采集的可微自适应四维结构光照
Differentiable Adaptive 4D Structured Illumination for Joint Capture of Shape and Reflectance
把「下一次投什么光」变成可微优化问题：用像素级深度不确定性直方图模型驱动自适应结构光图案选择，再用测量-仿真一致性微调联合恢复深度与反射率，可复用到主动式三维扫描与光度立体采集策略设计。
原文依据 we differentiably link the next illumination condition(s) with a loss that encourages the reduction in depth uncertainty
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出一个可微框架，针对物体自适应地计算四维光照条件，借助统一的空间-角度结构光与单台相机，高效、高质量地同时获取物体的形状与反射率。我们为深度与反射率采用一种简单的、基于直方图的像素级概率模型，以可微的方式将下一次（若干次）光照条件与一个鼓励降低深度不确定性的损失函数关联起来。当新的结构光被投射时，相应的图像测量被用于更新每个像素处的不确定性。最后，基于微调的方法通过最小化所有物理测量与其仿真对应值之间的差异，重建深度图与反射率参数图。我们在形状与外观差异很大的真实物体上验证了该框架的有效性。我们的深度结果与最优技术相比具有优势，而反射率结果在与照片对比验证时也与之相当。
英文原文摘要 1061 字符
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.