Publikation

Fast Projector-Driven Structured Light Matching in Sub-Pixel Accuracy using Bilinear Interpolation Assumption

Torben Fetzer, Gerd Reis, Didier Stricker

In: International Conference on Computer Analysis of Images and Patterns. International Conference on Computer Analysis of Images and Patterns (CAIP-2021) September 27-30 Online Springer LNCS 2021.

Abstrakt

In practical applications where high-precision reconstructions are required, whether for quality control or damage assessment, structured light reconstruction is often the method of choice. It allows to achieve dense point correspondences over the entire scene independently of any object texture. The optimal matches between images with respect to an encoded surface point are usually not on pixel but on sub-pixel level. Common matching techniques that look for pixel-to-pixel correspondences between camera and projector often lead to noisy results that must be subsequently smoothed. The method presented here allows to find optimal sub-pixel positions for each projector pixel in a single pass and thus requires minimal computational effort. For this purpose, the quadrilateral regions containing the sub-pixels are extracted. The convexity of these quads and their consistency in terms of topological properties can be guaranteed during runtime. Subsequently, an explicit formulation of the optimal sub-pixel position within each quad is derived, using bilinear interpolation, and the permanent existence of a valid solution is proven. In this way, an easy-to-use procedure arises that matches any number of cameras in a structured light setup with high accuracy and low complexity. Due to the ensured topological properties, exceptionally smooth, highly precise, uniformly sampled matches with almost no outliers are achieved. The point correspondences obtained do not only have an enormously positive effect on the accuracy of reconstructed point clouds and resulting meshes, but are also extremely valuable for auto-calibrations calculated from them.

Projekte

FastMatching.pdf (pdf, 9 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence