Convex bricks: A new primitive for visual hull modeling and reconstruction

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.