Related papers: Deep Kernelized Dense Geometric Matching

Deep Kernelized Dense Geometric Matching

URL: http://arxiv.org/abs/2202.00667v1
Date: Tue, 1 Feb 2022 18:58:46 GMT
Title: Deep Kernelized Dense Geometric Matching
Authors: Johan Edstedt, M{\aa}rten Wadenb\"ack, Michael Felsberg
Abstract summary: We propose to formulate global correspondence estimation as a continuous probabilistic regression task using deep kernels. Our approach achieves significant improvements compared to the state-of-the-art on the competitive HPatches and YFCC100m benchmarks.
Score: 14.274582421372308
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Dense geometric matching is a challenging computer vision task, requiring accurate correspondences under extreme variations in viewpoint and illumination, even for low-texture regions. In this task, finding accurate global correspondences is essential for later refinement stages. The current learning based paradigm is to perform global fixed-size correlation, followed by flattening and convolution to predict correspondences. In this work, we consider the problem from a different perspective and propose to formulate global correspondence estimation as a continuous probabilistic regression task using deep kernels, yielding a novel approach to learning dense correspondences. Our full approach, \textbf{D}eep \textbf{K}ernelized \textbf{M}atching, achieves significant improvements compared to the state-of-the-art on the competitive HPatches and YFCC100m benchmarks, and we dissect the gains of our contributions in a thorough ablation study.

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