Wire Structure Image-Based 3D Reconstruction Aided by Deep Learning
Research Collection Conference Paper Wire structure image-based 3d reconstruction aided by deep learning Author(s): Kniaz, Vladimir V.; Zheltov, Sergey Yu.; Remondino, Fabio; Knyaz, Vladimir A.; Gruen, Armin Publication Date: 2020 Permanent Link: https://doi.org/10.3929/ethz-b-000442466 Originally published in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII- B2-2020, http://doi.org/10.5194/isprs-archives-XLIII-B2-2020-435-2020 Rights / License: Creative Commons Attribution 4.0 International This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B2-2020, 2020 XXIV ISPRS Congress (2020 edition) WIRE STRUCTURE IMAGE-BASED 3D RECONSTRUCTION AIDED BY DEEP LEARNING Vladimir V. Kniaz1,2,∗ Sergey Yu. Zheltov1, Fabio Remondino3, Vladimir A. Knyaz1,2, Artyom Bordodymov1, Armin Gruen4 1 State Res. Institute of Aviation Systems (GosNIIAS), 125319, 7, Victorenko str., Moscow, Russia – (vl.kniaz, zhl, knyaz, bordodymov)@gosniias.ru 2 Moscow Institute of Physics and Technology (MIPT), Dolgoprudny, Russia 3 Bruno Kessler Foundation (FBK), Trento, Italy – remondino@fbk.eu 4 ETH Zurich, Switzerland – armin.gruen@geod.baug.ethz.ch Commission II, WG II/8 KEY WORDS: structure from motion, wire structures 3D reconstruction, segmentation, deep learning, Shukhov Radio tower ABSTRACT: Objects and structures realized by connecting and bending wires are common in modern architecture, furniture design, metal sculpting, etc. The 3D reconstruction of such objects with traditional range- or image-based methods is very difficult and poses challenges due to their unique characteristics such as repeated structures, slim elements, holes, lack of features, self-occlusions, etc.
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