Authors:
Bruce Canovas
;
Michele Rombaut
;
Amaury Negre
;
Serge Olympieff
and
Denis Pellerin
Affiliation:
Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble and France
Keyword(s):
RGB-D, Dense Reconstruction, Superpixel, Surfel, Fusion, Robotics.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
Abstract:
In this paper we present a novel lightweight and simple 3D representation for real-time dense 3D mapping of static environments with an RGB-D camera. Our approach builds and updates a low resolution 3D model of an observed scene as an unordered set of new primitives called supersurfels, which can be seen as elliptical planar patches, generated from superpixels segmented RGB-D live measurements. While most of the actual solutions focuse on the accuracy of the reconstructed 3D model, the implemented method is well-adapted to run on robots with reduced/limited computing capacity and memory size, which do not need a highly detailed map of their environment but can settle for an approximate one.