Efficient Single-View 3D Co-segmentation Using Shape Similarity and Spatial Part Relations

The practical use of the latest methods for supervised 3D shape co-segmentation is limited by the requirement of diverse training data and a watertight mesh representation. Driven by practical considerations, we assume only one reference shape to be available and the query shape to be provided as a partially visible point cloud. We propose a novel co-segmentation approach that constructs a part-based object representation comprised of shape appearance models of individual parts and isometric spatial relations between the parts. The partial query shape is pre-segmented using planar cuts, and the segments accompanied by the learned representation induce a compact Conditional Random Field (CRF). CRF inference is performed efficiently by A∗-search with global optimality guarantees. A comparative evaluation with two baselines on partial views generated from the Labelled Princeton Segmentation Benchmark and point clouds recorded with an RGB-D sensor demonstrate superiority of the proposed approach both in accuracy and efficiency.