论文标题
带有神经表面的近光照光度立体声
Edge-preserving Near-light Photometric Stereo with Neural Surfaces
论文作者
论文摘要
本文提出了一种接近光度的立体声方法，该方法忠实地保留了3D重建中的尖锐深度边缘。与以前依靠有限分化来近似深度部分衍生物和表面正常的方法不同，我们引入了近光度光度立体声中的分析上可区分的神经表面，以避免在尖锐的深度边缘处的分化误差，其中深度表示图像坐标的神经功能。通过进一步将兰伯特式反映物作为由表面正常和深度产生的因变量，我们的方法不准确地深度初始化。在合成场景和现实世界中进行的实验证明了我们方法在边缘保存中详细形状恢复的有效性。
This paper presents a near-light photometric stereo method that faithfully preserves sharp depth edges in the 3D reconstruction. Unlike previous methods that rely on finite differentiation for approximating depth partial derivatives and surface normals, we introduce an analytically differentiable neural surface in near-light photometric stereo for avoiding differentiation errors at sharp depth edges, where the depth is represented as a neural function of the image coordinates. By further formulating the Lambertian albedo as a dependent variable resulting from the surface normal and depth, our method is insusceptible to inaccurate depth initialization. Experiments on both synthetic and real-world scenes demonstrate the effectiveness of our method for detailed shape recovery with edge preservation.
