EVALUATING HAND-CRAFTED AND LEARNING-BASED FEATURES FOR PHOTOGRAMMETRIC APPLICATIONS

نویسندگان

چکیده

Abstract. The image orientation (or Structure from Motion – SfM) process needs well localized, repeatable and stable tie points in order to derive camera poses a sparse 3D representation of the surveyed scene. accurate identification large datasets is still an open research topic photogrammetric computer vision communities. Tie are established by firstly extracting keypoint using hand-crafted feature detector descriptor methods. In last years new solutions, based on convolutional neural network (CNN) methods, were proposed let deep discover which extraction most suitable for processed images. this paper we aim compare state-of-the-art learning-based method establishment various different datasets. investigation highlights actual challenges matching evaluates selected methods under acquisition conditions (network configurations, overlap, UAV vs terrestrial, strip convergent) scene's characteristics. Remarks lessons learned constrained used provided.

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ژورنال

عنوان ژورنال: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

سال: 2021

ISSN: ['1682-1777', '1682-1750', '2194-9034']

DOI: https://doi.org/10.5194/isprs-archives-xliii-b2-2021-549-2021