Geometry reconstruction of textureless, non-Lambertian objects under unknown natural illumination (i.e., in the wild) remains challenging as correspondences cannot be established and the reflectance cannot be expressed in simple analytical forms. We derive a novel multi-view method, DeepShaRM, that achieves state-of-the-art accuracy on this challenging task. Unlike past methods that formulate this as inverse-rendering, i.e., estimation of reflectance, illumination, and geometry from images, our key idea is to realize that reflectance and illumination need not be disentangled and instead estimated as a compound reflectance map. We introduce a novel deep reflectance map estimation network that recovers the camera-view reflectance maps from the surface normals of the current geometry estimate and the input multi-view images. The network also explicitly estimates per-pixel confidence scores to handle global light transport effects. A deep shape-from-shading network then updates the geometry estimate expressed with a signed distance function using the recovered reflectance maps. By alternating between these two, and, most important, by bypassing the ill-posed problem of reflectance and illumination decomposition, the method accurately recovers object geometry in these challenging settings. Extensive experiments on both synthetic and real-world data clearly demonstrate its state-of-the-art accuracy.

翻译：在未知自然光照（即野外环境）下，对无纹理、非朗伯物体的几何重建仍然具有挑战性，因为无法建立对应点，且反射率无法用简单解析形式表示。我们提出了一种新颖的多视角方法DeepShaRM，在该困难任务上实现了最先进的精度。与以往将其视为逆渲染（即从图像中估计反射率、光照和几何）的方法不同，我们的核心思想是认识到反射率和光照无需解耦，而应作为复合反射率图进行估计。我们引入了一种新颖的深度反射率图估计网络，该网络根据当前几何估计的表面法向和输入的多视角图像恢复相机视角的反射率图。该网络还显式地估计每个像素的置信度分数以处理全局光传输效应。随后，一个深度明暗形状恢复网络利用恢复的反射率图更新以符号距离函数表达的几何估计。通过交替执行这两个步骤，并且至关重要的是，通过绕过反射率和光照分解这一病态问题，该方法在这些困难场景下准确恢复了物体的几何形状。在合成数据和真实数据上的大量实验明确证明了其最先进的精度。