Surface-from-Gradients without Discrete Integrability Enforcement: A Gaussian Kernel Approach

—Representative surface reconstruction algorithms taking a gradient field as input enforces the integrability constraint in a discrete manner. While enforcing integrability allows the subsequent integration to produce surface heights, existing algorithms have one or more of the following disadvantages: they can only handle dense per-pixel gradient fields, smooth out sharp features in a partially integrable field, or produce severe surface distortion in the results. In this paper, we present a method which does not enforce discrete integrability, and reconstructs a 3D continuous surface from a gradient or a height field, or a combination of both, which can be dense or sparse. The key of our approach is the use of kernel basis functions, which transfers the continuous surface reconstruction problem into high dimensional space where a closed-form solution exists. By using the Gaussian kernel, we can derive a straightforward implementation which is able produce results better than tr...

• Type: Journal
• Year: 2010
• Where: PAMI
• Authors: Heung-Sun Ng, Tai-Pang Wu, Chi-Keung Tang
• Keywords: PAMI 2010, Sharp Features, Surface Reconstruction, —Representative Surface Reconstruction