Conference proceeding
Learning Implicit Functions for Dense 3D Shape Correspondence of Generic Objects
IEEE transactions on pattern analysis and machine intelligence, v 46(3), pp 1852-1867
01 Mar 2024
PMID: 37018312
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The objective of this paper is to learn dense 3D shape correspondence for topology-varying generic objects in an unsupervised manner. Conventional implicit functions estimate the occupancy of a 3D point given a shape latent code. Instead, our novel implicit function produces a probabilistic embedding to represent each 3D point in a part embedding space. Assuming the corresponding points are similar in the embedding space, we implement dense correspondence through an inverse function mapping from the part embedding vector to a corresponded 3D point. Both functions are jointly learned with several effective and uncertainty-aware loss functions to realize our assumption, together with the encoder generating the shape latent code. During inference, if a user selects an arbitrary point on the source shape, our algorithm can automatically generate a confidence score indicating whether there is a correspondence on the target shape, as well as the corresponding semantic point if there is one. Such a mechanism inherently benefits man-made objects with different part constitutions. The effectiveness of our approach is demonstrated through unsupervised 3D semantic correspondence and shape segmentation.
Metrics
Details
- Title
- Learning Implicit Functions for Dense 3D Shape Correspondence of Generic Objects
- Creators
- Feng Liu - Drexel University, Computer ScienceXiaoming Liu - Michigan State University
- Publication Details
- IEEE transactions on pattern analysis and machine intelligence, v 46(3), pp 1852-1867
- Publisher
- IEEE
- Number of pages
- 16
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:001174191100006
- Scopus ID
- 2-s2.0-85147208305
- Other Identifier
- 991021906502304721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
InCites Highlights
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- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic