High Quality Disparity Remapping with Two-Stage Warping

Bing Li, Chia-Wen Lin, Cheng Zheng, Shan Liu, Junsong Yuan, Bernard Ghanem, C.-C. Jay Kuo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A high quality disparity remapping method that preserves 2D shapes and 3D structures, and adjusts disparities of important objects in stereo image pairs is proposed. It is formulated as a constrained optimization problem, whose solution is challenging, since we need to meet multiple requirements of disparity remapping simultaneously. The one-stage optimization process either degrades the quality of important objects or introduces serious distortions in background regions. To address this challenge, we propose a two-stage warping process to solve it. In the first stage, we develop a warping model that finds the optimal warping grids for important objects to fulfill multiple requirements of disparity remapping. In the second stage, we derive another warping model to refine warping results in less important regions by eliminating serious distortions in shape, disparity and 3D structure. The superior performance of the proposed method is demonstrated by experimental results.
Original languageEnglish (US)
Title of host publication2021 IEEE/CVF International Conference on Computer Vision (ICCV)
PublisherIEEE
ISBN (Print)978-1-6654-2813-2
DOIs
StatePublished - 2021

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