all AI news
CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning. (arXiv:2201.08215v1 [cs.CV])
Jan. 21, 2022, 2:10 a.m. | Mingye Xu, Zhipeng Zhou, Hongbin Xu, Yali Wang, Yu Qiao
cs.CV updates on arXiv.org arxiv.org
Self-supervised learning has not been fully explored for point cloud
analysis. Current frameworks are mainly based on point cloud reconstruction.
Given only 3D coordinates, such approaches tend to learn local geometric
structures and contours, while failing in understanding high level semantic
content. Consequently, they achieve unsatisfactory performance in downstream
tasks such as classification, segmentation, etc. To fill this gap, we propose a
generic Contour-Perturbed Reconstruction Network (CP-Net), which can
effectively guide self-supervised reconstruction to learn semantic content in
the point …
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Principal Data Engineer
@ RS21 | Remote
SQL/Power BI Developer
@ ICF | Virginia Remote Office (VA99)
Senior Machine Learning Engineer (Canada Remote)
@ Fullscript | Ottawa, ON
Software Engineer - MLOps.
@ Renesas Electronics | Toyosu, Japan
Junior Data Scientist / Artificial Intelligence consultant
@ Deloitte | Luxembourg, LU