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Issue No. 11 - November (2010 vol. 32)
ISSN: 0162-8828
pp: 2085-2099
Heung-Sun Ng , Hong Kong University of Science and Technology, Hong Kong
Tai-Pang Wu , Hong Kong University of Science and Technology, Hong Kong
Chi-Keung Tang , Hong Kong University of Science and Technology, Hong Kong
Representative surface reconstruction algorithms taking a gradient field as input enforce 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 to our approach is the use of kernel basis functions, which transfer 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 to produce results better than traditional techniques. In general, an important advantage of our kernel-based method is that the method does not suffer discretization and finite approximation, both of which lead to surface distortion, which is typical of Fourier or wavelet bases widely adopted by previous representative approaches. We perform comparisons with classical and recent methods on benchmark as well as challenging data sets to demonstrate that our method produces accurate surface reconstruction that preserves salient and sharp features. The source code and executable of the system are available for downloading.
Surface from gradients, integrability, kernel methods, basis functions.

C. Tang, T. Wu and H. Ng, "Surface-from-Gradients without Discrete Integrability Enforcement: A Gaussian Kernel Approach," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 2085-2099, 2009.
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