The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 405–412, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-405-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 405–412, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-405-2021

  28 Jun 2021

28 Jun 2021

A NEW STEREO DENSE MATCHING BENCHMARK DATASET FOR DEEP LEARNING

T. Wu, B. Vallet, M. Pierrot-Deseilligny, and E. Rupnik T. Wu et al.
  • LASTIG, Univ Gustave Eiffel, ENSG, IGN, F-94160 Saint-Mandé, France

Keywords: stereo dense matching, deep learning, benchmarking, LiDAR processing, 3D reconstruction

Abstract. Stereo dense matching is a fundamental task for 3D scene reconstruction. Recently, deep learning based methods have proven effective on some benchmark datasets, for example Middlebury and KITTI stereo. However, it is not easy to find a training dataset for aerial photogrammetry. Generating ground truth data for real scenes is a challenging task. In the photogrammetry community, many evaluation methods use digital surface models (DSM) to generate the ground truth disparity for the stereo pairs, but in this case interpolation may bring errors in the estimated disparity. In this paper, we publish a stereo dense matching dataset based on ISPRS Vaihingen dataset, and use it to evaluate some traditional and deep learning based methods. The evaluation shows that learning-based methods outperform traditional methods significantly when the fine tuning is done on a similar landscape. The benchmark also investigates the impact of the base to height ratio on the performance of the evaluated methods. The dataset can be found in https://github.com/whuwuteng/benchmark_ISPRS2021.