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Multi-Label Learning based Semi-Global Matching Forest

Department of Photogrammetry and Image Analysis, Remote Sensing Technology Institute, German Aerospace Center (DLR), 82234 Wessling, Germany
Institute of Computer Graphics and Vision, Graz University of Technology (TU Graz), 8010 Graz, Austria
Author to whom correspondence should be addressed.
Received: 17 March 2020 / Accepted: 24 March 2020 / Published: 26 March 2020
(This article belongs to the Special Issue Point Cloud Processing in Remote Sensing)
Semi-Global Matching (SGM) approximates a 2D Markov Random Field (MRF) via multiple 1D scanline optimizations, which serves as a good trade-off between accuracy and efficiency in dense matching. Nevertheless, the performance is limited due to the simple summation of the aggregated costs from all 1D scanline optimizations for the final disparity estimation. SGM-Forest improves the performance of SGM by training a random forest to predict the best scanline according to each scanline’s disparity proposal. The disparity estimated by the best scanline acts as reference to adaptively adopt close proposals for further post-processing. However, in many cases more than one scanline is capable of providing a good prediction. Training the random forest with only one scanline labeled may limit or even confuse the learning procedure when other scanlines can offer similar contributions. In this paper, we propose a multi-label classification strategy to further improve SGM-Forest. Each training sample is allowed to be described by multiple labels (or zero label) if more than one (or none) scanline gives a proper prediction. We test the proposed method on stereo matching datasets, from Middlebury, ETH3D, EuroSDR image matching benchmark, and the 2019 IEEE GRSS data fusion contest. The result indicates that under the framework of SGM-Forest, the multi-label strategy outperforms the single-label scheme consistently. View Full-Text
Keywords: Semi-Global Matching (SGM); random forests; scanline; multi-label classification; disparity; learning Semi-Global Matching (SGM); random forests; scanline; multi-label classification; disparity; learning
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MDPI and ACS Style

Xia, Y.; d’Angelo, P.; Tian, J.; Fraundorfer, F.; Reinartz, P. Multi-Label Learning based Semi-Global Matching Forest. Remote Sens. 2020, 12, 1069.

AMA Style

Xia Y, d’Angelo P, Tian J, Fraundorfer F, Reinartz P. Multi-Label Learning based Semi-Global Matching Forest. Remote Sensing. 2020; 12(7):1069.

Chicago/Turabian Style

Xia, Yuanxin, Pablo d’Angelo, Jiaojiao Tian, Friedrich Fraundorfer, and Peter Reinartz. 2020. "Multi-Label Learning based Semi-Global Matching Forest" Remote Sensing 12, no. 7: 1069.

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