Robo-vision! 3D mesh generation of a scene for a robot for planar and non-planar complex objects

This paper presents a novel architecture to generate a world model in terms of mesh from a continuous image stream with depth information extracted from a robot's ego-vision camera. We propose two algorithms for planar and non-planar mesh generation. A Cartesian grid-based mesh fitting algorithm is proposed for mesh generation of planar objects. For mesh generation of non-planar objects, we propose a Self Organization Map based algorithm. The proposed algorithm better approaches the boundary and overall shape of the objects compared to State-Of-the-Art (SOA). Extensive experiments done on three public datasets show that our method surpasses SOA both qualitatively and quantitatively.