Shape-Graph Matching Network (SGM-net): Registration for Statistical Shape Analysis
Shenyuan Liang, Mauricio Pamplona Segundo, Sathyanarayanan N. Aakur,, Sudeep Sarkar, Anuj Srivastava

TL;DR
This paper introduces SGM-net, a neural network architecture for registering shape graphs, achieving state-of-the-art accuracy and significantly reduced computational cost in statistical shape analysis.
Contribution
The paper presents a novel neural network approach with an unsupervised elastic shape metric loss for shape graph registration, improving performance and efficiency.
Findings
Achieves state-of-the-art matching accuracy
Reduces computational cost by an order of magnitude
Effective on both simulated and real-world data
Abstract
This paper focuses on the statistical analysis of shapes of data objects called shape graphs, a set of nodes connected by articulated curves with arbitrary shapes. A critical need here is a constrained registration of points (nodes to nodes, edges to edges) across objects. This, in turn, requires optimization over the permutation group, made challenging by differences in nodes (in terms of numbers, locations) and edges (in terms of shapes, placements, and sizes) across objects. This paper tackles this registration problem using a novel neural-network architecture and involves an unsupervised loss function developed using the elastic shape metric for curves. This architecture results in (1) state-of-the-art matching performance and (2) an order of magnitude reduction in the computational cost relative to baseline approaches. We demonstrate the effectiveness of the proposed approach using…
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Taxonomy
Topics3D Shape Modeling and Analysis · Graph Theory and Algorithms · Image Processing and 3D Reconstruction
