Structure-oriented Networks of Shape Collections

Noa Fish1   Oliver van Kaick2   Amit Bermano3   Daniel Cohen-Or1
1Tel Aviv University   2Carleton University   3Princeton University

SIGGRAPH Asia 2016


We introduce a co-analysis technique designed for correspondence inference within large shape collections. Such collections are naturally rich in variation, adding ambiguity to the notoriously difficult problem of correspondence computation. We leverage the robustness of correspondences between similar shapes to address the difficulties associated with this problem. In our approach, pairs of similar shapes are extracted from the collection, analyzed and matched in an efficient and reliable manner, culminating in the construction of a network of correspondences that connects the entire collection. The correspondence between any pair of shapes then amounts to a simple propagation along the minimax path between the two shapes in the network. At the heart of our approach is the introduction of a robust, structure-oriented shape matching method. Leveraging the idea of projective analysis, we partition 2D projections of a shape to obtain a set of 1D ordered regions, which are both simple and efficient to match. We lift the matched projections back to the 3D domain to obtain a pairwise shape correspondence. The emphasis given to structural compatibility is a central tool in estimating the reliability and completeness of a computed correspondence, uncovering any non-negligible semantic discrepancies that may exist between shapes. These detected differences are a deciding factor in the establishment of a network aiming to capture local similarities. We demonstrate that the combination of the presented observations into a co-analysis method allows us to establish reliable correspondences among shapes within large collections.


Paper: paper (PDF, 3.8MB), supplementary material (PDF, 5.6MB)
Code: MATLAB code
Data: ShapeNet, COSEG


We thank the anonymous reviewers for their helpful suggestions.This work was supported by a Google Focused Research Award and NSERC (2015-05407).