Learned Collaborative Stereo Refinement

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

In this work, we propose a learning-based method to denoise and refine disparity maps of a given stereo method. The proposed variational network arises naturally from unrolling the iterates of a proximal gradient method applied to a variational energy defined in a joint disparity, color, and confidence image space. Our method allows to learn a robust collaborative regularizer leveraging the joint statistics of the color image, the confidence map and the disparity map. Due to the variational structure of our method, the individual steps can be easily visualized, thus enabling interpretability of the method. We can therefore provide interesting insights into how our method refines and denoises disparity maps. The efficiency of our method is demonstrated by the publicly available stereo benchmarks Middlebury 2014 and Kitti 2015.
Originalspracheenglisch
TitelGerman Conference on Pattern Recognition
Seiten3-17
PublikationsstatusVeröffentlicht - 2019

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Color
Gradient methods
Statistics

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Knöbelreiter, P., & Pock, T. (2019). Learned Collaborative Stereo Refinement. in German Conference on Pattern Recognition (S. 3-17)

Learned Collaborative Stereo Refinement. / Knöbelreiter, Patrick; Pock, Thomas.

German Conference on Pattern Recognition. 2019. S. 3-17.

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Knöbelreiter, P & Pock, T 2019, Learned Collaborative Stereo Refinement. in German Conference on Pattern Recognition. S. 3-17.
Knöbelreiter P, Pock T. Learned Collaborative Stereo Refinement. in German Conference on Pattern Recognition. 2019. S. 3-17
Knöbelreiter, Patrick ; Pock, Thomas. / Learned Collaborative Stereo Refinement. German Conference on Pattern Recognition. 2019. S. 3-17
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