Contrastive Learning for 3D Point Clouds Classification and Shape Completion

In this paper, we present the idea of Self Supervised learning on the shape completion and classification of point clouds. Most 3D shape completion pipelines utilize AutoEncoders to extract features from point clouds used in downstream tasks such as classification, segmentation, detection, and other...

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Detalles Bibliográficos
Autores principales: Nazir, Danish, Afzal, Muhammad Zeshan, Pagani, Alain, Liwicki, Marcus, Stricker, Didier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587100/
https://www.ncbi.nlm.nih.gov/pubmed/34770698
http://dx.doi.org/10.3390/s21217392

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587100/
https://www.ncbi.nlm.nih.gov/pubmed/34770698
http://dx.doi.org/10.3390/s21217392

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