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The integration of surface normals for the purpose of computing the shape ofa surface in 3D space is a classic problem in computer vision. However, evennowadays it is still a challenging task to devise a method that combines theflexibility to work on non-trivial computational domains with high accuracy,robustness and computational efficiency. By uniting a classic approach forsurface normal integration with modern computational techniques we construct asolver that fulfils these requirements. Building upon the Poisson integrationmodel we propose to use an iterative Krylov subspace solver as a core step intackling the task. While such a method can be very efficient, it may only showits full potential when combined with a suitable numerical preconditioning anda problem-specific initialisation. We perform a thorough numerical study inorder to identify an appropriate preconditioner for our purpose. To address theissue of a suitable initialisation we propose to compute this initial state viaa recently developed fast marching integrator. Detailed numerical experimentsilluminate the benefits of this novel combination. In addition, we show onreal-world photometric stereo datasets that the developed numerical frameworkis flexible enough to tackle modern computer vision applications.

Fast and Accurate Surface Normal Integration on Non-Rectangular Domains