
Learning-based stereo matching has recently achieved promising results, yet still suffers difficulties in establishing reliable matches in weakly matchable regions that are textureless, non-Lambertian, or occluded. In this paper, we address this challenge by proposing a stereo matching network that considers pixel-wise matchability. Specifically, the network jointly regresses disparity and matchability maps from 3D probability volume through expectation and entropy operations. Next, a learned attenuation is applied as the robust loss function to alleviate the influence of weakly matchable pixels in the training. Finally, a matchability-aware disparity refinement is introduced to improve the depth inference in weakly matchable regions. The proposed deep stereo matchability (DSM) framework can
improve the matching result or accelerate the computation while still guaranteeing the quality. Moreover, the DSM framework is portable to many recent stereo networks. Extensive experiments are conducted on Scene Flow and KITTI stereo datasets to demonstrate the effectiveness of the proposed framework over the state-of-the-art learning-based stereo methods.

Learning Stereo Matchability in Disparity Regression Networks


A 3layered skin model is built to simulate hyperspectral reflectance using Monte Carlo simulations based on biological components, which include melanin volume fraction, water level, blood volume fraction, oxygen saturation, etc. A forward neural network is trained for mapping biological components and reflectance. Then a database, which contains 50,000 samples spectra from 450 to 750 nm with randomly given biological components' information, are generated by this forward neural network. Support vector regression, inverse neural networks and random forest are applied for the regression analysis of reflectance data and melanin volume fraction. Dimensionality reduction is used to accelerate the training time. The performances of three regression methods are measured and show promising prediction results.

Fast Determination of Melanin based on Skin Hyperspectral Reflectance

Learning-based stereo matching has recently achieved promising results, yet still suffers difficulties in establishing reliable matches in weakly matchable regions that are textureless, non-Lambertian, or occluded. In this paper, we address this challenge by proposing a stereo matching network that considers pixel-wise matchability. Specifically, the network jointly regresses disparity and matchability maps from 3D probability volume through expectation and entropy operations. Next, a learned attenuation is applied as the robust loss function to alleviate the influence of weakly matchable pixels in the training. Finally, a matchability-aware disparity refinement is introduced to improve the depth inference in weakly matchable regions. The proposed deep stereo matchability (DSM) framework can
improve the matching result or accelerate the computation while still guaranteeing the quality. Moreover, the DSM framework is portable to many recent stereo networks. Extensive experiments are conducted on Scene Flow and KITTI stereo datasets to demonstrate the effectiveness of the proposed framework over the state-of-the-art learning-based stereo methods.

**Session Chairs**: prof. Andrea Fusiello, prof. Alessio del Bue

OS T3.3: Stereo and 3D Vision

technical paper

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Shiwei Li

3

SHORT BIO

Presentations

Learning Stereo Matchability in Disparity Regression Networks

Fast Determination of Melanin based on Skin Hyperspectral Reflectance

Learning Stereo Matchability in Disparity Regression Networks

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