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Recent years have witnessed remarkable success of Graph Fourier Transform (GFT) in point cloud attribute compression. Existing researches mainly utilize geometry distance to define graph structure for coding attribute (e.g., color), which may distribute high weights to the edges connecting points across texture boundaries.
Beside the minimizationof the prediction error, two of the most desirable properties of a regression scheme are stability and interpretability . Driven by these principles, we propose continuous-domain formulations for one-dimensional regression problems. In our first approach, we use the Lipschitz constant as a regularizer, which results in an implicit tuning of the overall robustness of the learned mapping.
Date: September 26-28, 2022
Location: Shanghai, China
Submission Deadline: May 30, 2022
Call for Proposals Document
Date: October 26-November 11, 2022
Location: Yokohama, Japan
CFP Announcement
Submission Deadline: April 10, 2022
Call for Proposals Document
Manuscript Due: 10 December 2022
Publication Date: September 2023
CFP Document