Publication
An optimization approach for extracting and encoding consistent maps in a shape collection
AbstractWe introduce a novel approach for computing high quality point-to-point maps among a collection of related shapes. The proposed approach takes as input a sparse set of imperfect initial maps between pairs of shapes and builds a compact data structure which implicitly encodes an improved set of maps between all pairs of shapes. These maps align well with point correspondences selected from initial maps; they map neighboring points to neighboring points; and they provide cycle-consistency, so that map compositions along cycles approximate the identity map. The proposed approach is motivated by the fact that a complete set of maps between all pairs of shapes that admits nearly perfect cycle-consistency are highly redundant and can be represented by compositions of maps through a single base shape. In general, multiple base shapes are needed to adequately cover a diverse collection. Our algorithm sequentially extracts such a small collection of base shapes and creates correspondences from each of these base shapes to all other shapes. These correspondences are found by global optimization on candidate correspondences obtained by diffusing initial maps. These are then used to create a compact graphical data structure from which globally optimal cycle-consistent maps can be extracted using simple graph algorithms. Experimental results on benchmark datasets show that the proposed approach yields significantly better results than state-of-the-art data-driven shape matching methods.
Download publicationRelated Resources
See what’s new.
2023
Challenges in Extracting Insights from Life Cycle Assessment Documents During Early Stage DesignKnowledge transfer from LCA documents and building a structured…
2024
Experiential Views: Towards Human Experience Evaluation of Designed Spaces using Vision-Language ModelsExploratory research on helping designers and architects anticipate…
2019
Relational Graph Representation Learning for Open-Domain Question AnsweringWe introduce a relational graph neural network with bi-directional…
2009
The challenge of irrationality: fractal protein recipes for PIComputational development traditionally focuses on the use of an…
Get in touch
Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.
Contact us