Abstract
Our aim is to provide an autonomous vehicle moving into an indoor environment with a visual system to perform a qualitative 3D structure reconstruction of the surrounding environment by recovering the different planar surfaces present in the observed scene.
The method is based on qualitative detection of planar surfaces by using projective invariant constraints without the use of depth estimates. The goal is achieved by analyzing two images acquired by observing the scene from two different points of view. The method can be applied to both stereo images and motion images.
Our method recovers planar surfaces by clustering high variance interest points whose cross ratio measurements are preserved in two different perspective projections. Once interest points are extracted from each image, the clustering process requires to grouping corresponding points by preserving the cross ratio measurements.
We solve the twofold problem of finding corresponding points and grouping the coplanar ones through a global optimization approach based on matching of high relational graphs and clustering on the corresponding association graph through a relaxation labeling algorithm.
Through our experimental tests, we found the method to be very fast to converge to a solution, showing how higher order interactions, instead to giving rise to a more complex problem, help to speed-up the optimization process and to reach at same time good results.
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Branca, A., Stella, E. & Distante, A. Qualitative Scene Interpretation Using Planar Surfaces. Autonomous Robots 8, 129–139 (2000). https://doi.org/10.1023/A:1008931527373
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DOI: https://doi.org/10.1023/A:1008931527373