P ri F it : Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation

Author:

Sharma G.1ORCID,Dash B.1ORCID,RoyChowdhury A.1,Gadelha M.12ORCID,Loizou M.3ORCID,Cao L.1,Wang R.1,Learned‐Miller E. G.1ORCID,Maji S.1ORCID,Kalogerakis E.1ORCID

Affiliation:

1. University of Massachusetts Amherst

2. Adobe

3. University of Cyprus

Funder

National Stroke Foundation

Publisher

Wiley

Subject

Computer Graphics and Computer-Aided Design

Reference62 articles.

1. Alliegro Antonio Boscaini Davide andTommasi Tatiana.Joint Supervised and Self‐Supervised Learning for 3D Real‐World Challenges.20203 7.

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3. Binford I.“Visual perception by computer”.IEEE Conference of Systems and Control.19712.

4. Binford Thomas O Levitt Tod S andMann Wallace B.“Bayesian inference in model‐based machine vision”.Proceedings of the Third Conference on Uncertainty in Artificial Intelligence.19872.

5. Carr Jonathan C Beatson Richard K Cherrie Jon B et al. “Reconstruction and representation of 3D objects with radial basis functions”.Proceedings of the 28th annual conference on Computer graphics and interactive techniques.20012.

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1. Iterative Superquadric Recomposition of 3D Objects from Multiple Views;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

2. PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

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5. Few-shot 3D Point Cloud Semantic Segmentation with Prototype Alignment;Proceedings of the 2023 8th International Conference on Machine Learning Technologies;2023-03-10

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