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UNCERTAINTY ESTIMATION FOR END-TO-END LEARNED DENSE STEREO MATCHING VIA PROBABILISTIC DEEP LEARNING
2020
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Abstract. Motivated by the need to identify erroneous disparity assignments, various approaches for uncertainty and confidence estimation of dense stereo matching have been presented in recent years. As in many other fields, especially deep learning based methods have shown convincing results. However, most of these methods only model the uncertainty contained in the data, while ignoring the uncertainty of the employed dense stereo matching procedure. Additionally modelling the latter, however,
doi:10.5194/isprs-annals-v-2-2020-161-2020
fatcat:ks32kkzo3zgzfprfne27xhmdze