3D Keypoint Estimation Using Implicit Representation Learning

dc.contributor.authorZhu, Xiangyuen_US
dc.contributor.authorDu, Dongen_US
dc.contributor.authorHuang, Haibinen_US
dc.contributor.authorMa, Chongyangen_US
dc.contributor.authorHan, Xiaoguangen_US
dc.contributor.editorMemari, Pooranen_US
dc.contributor.editorSolomon, Justinen_US
dc.date.accessioned2023-06-30T06:19:18Z
dc.date.available2023-06-30T06:19:18Z
dc.date.issued2023
dc.description.abstractIn this paper, we tackle the challenging problem of 3D keypoint estimation of general objects using a novel implicit representation. Previous works have demonstrated promising results for keypoint prediction through direct coordinate regression or heatmap-based inference. However, these methods are commonly studied for specific subjects, such as human bodies and faces, which possess fixed keypoint structures. They also suffer in several practical scenarios where explicit or complete geometry is not given, including images and partial point clouds. Inspired by the recent success of advanced implicit representation in reconstruction tasks, we explore the idea of using an implicit field to represent keypoints. Specifically, our key idea is employing spheres to represent 3D keypoints, thereby enabling the learnability of the corresponding signed distance field. Explicit keypoints can be extracted subsequently by our algorithm based on the Hough transform. Quantitative and qualitative evaluations also show the superiority of our representation in terms of prediction accuracy.en_US
dc.description.number5
dc.description.sectionheadersRepresentation and Learning
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14917
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14917
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14917
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies -> Shape analysis; Shape representations
dc.subjectComputing methodologies
dc.subjectShape analysis
dc.subjectShape representations
dc.title3D Keypoint Estimation Using Implicit Representation Learningen_US
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