ICRA 2012 Paper Abstract

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Paper TuB08.4

Chari, Visesh (INRIA Rhône-Alpes), Agrawal, Amit (Mitsubishi Electric Research Labs), Taguchi, Yuichi (Mitsubishi Electric Research Laboratories), Ramalingam, Srikumar (Mitsubishi Electric Research Lab)

Convex Bricks: A New Primitive for Visual Hull Modeling and Reconstruction

Scheduled for presentation during the Regular Session "3D Surface Models, Point Cloud Processing" (TuB08), Tuesday, May 15, 2012, 11:15−11:30, Meeting Room 8 (Wacipi)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on June 22, 2018

Keywords Computer Vision for Robotics and Automation, Localization, Visual Servoing

Abstract

Industrial automation tasks typically require a 3D model of the object for robotic manipulation. The ability to reconstruct the 3D model using a sample object is useful when CAD models are not available. For textureless objects, visual hull of the object obtained using silhouette-based reconstruction can avoid expensive 3D scanners for 3D modeling. We propose convex brick (CB), a new 3D primitive for modeling and reconstructing a visual hull from silhouettes. CB's are powerful in modeling arbitrary non-convex 3D shapes. Using CB, we describe an algorithm to generate a polyhedral visual hull from polygonal silhouettes; the visual hull is reconstructed as a combination of 3D convex bricks. Our approach uses well-studied geometric operations such as 2D convex decomposition and intersection of 3D convex cones using linear programming. The shape of CB can adapt to the given silhouettes, thereby significantly reducing the number of primitives required for a volumetric representation. Our framework allows easy control of reconstruction parameters such as accuracy and the number of required primitives. We present an extensive analysis of our algorithm and show visual hull reconstruction on challenging real datasets consisting of highly non-convex shapes. We also show real results on pose estimation of an industrial part in a bin-picking system using the reconstructed visual hull.

 

 

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