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Dense 3D visual mapping estimates as many as possible pixel depths, for eachimage. This results in very dense point clouds that often contain redundant andnoisy information, especially for surfaces that are roughly planar, forinstance, the ground or the walls in the scene. In this paper we leverage onsemantic image segmentation to discriminate which regions of the scene requiresimplification and which should be kept at high level of details. We proposefour different point cloud simplification methods which decimate the perceivedpoint cloud by relying on class-specific local and global statistics stillmaintaining more points in the proximity of class boundaries to preserve theinfra-class edges and discontinuities. 3D dense model is obtained by fusing thepoint clouds in a 3D Delaunay Triangulation to deal with variable point clouddensity. In the experimental evaluation we have shown that, by leveraging onsemantics, it is possible to simplify the model and diminish the noiseaffecting the point clouds.

Dense 3D Visual Mapping via Semantic Simplification