Functional correspondence by matrix completion
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Abstract (arXiv)
In this paper, we consider the problem of finding dense intrinsic correspondence between manifolds using the recently introduced functional framework. We pose the functional correspondence problem as matrix completion with manifold geometric structure and inducing functional localization with the L1 norm. We discuss efficient numerical procedures for the solution of our problem. Our method compares favorably to the accuracy of state-of-the-art correspondence algorithms on non-rigid shape matching benchmarks, and is especially advantageous in settings when only scarce data is available.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Dec 2014.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.
3 papers.
- 2025Hierarchical Neural Semantic Representation for 3D Semantic Correspondencecited by 14
- 2024SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimationcited by 12
- 2022Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformationscited by 2
- 2025Hierarchical Neural Semantic Representation for 3D Semantic Correspondence22 Sep 2025
- 2024SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimation18 Sep 2024
- 2022Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations5 Sep 2022