
XXIV ISPRS Congress
Virtual Event of the 2020 Presentations
Nice, France, 31.08.2020 - 02.09.2020
Organizer: Société Française de Photogrammétrie et de Télédétection / Nicolas Paparoditis
Type of Event: Congress
Multi Media Type: Remote
Session: TU.2.1
Advancements in photogrammetric processing
Video of the whole session
Single Presentations

Image pre-processing strategies for enhancing photogrammetric 3D reconstruction of underwater shipwreck datasets
Corresponding paper: IMAGE PRE-PROCESSING STRATEGIES FOR ENHANCING PHOTOGRAMMETRIC 3D RECONSTRUCTION OF UNDERWATER SHIPWRECK DATASETS
Auhor(s): A. Calantropio, F. Chiabrando, B. Seymour, E. Kovacs, E. Lo, and D. Rissolo
Volume: Archives XLIII-B2-2020 / 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-941-2020

Lock-free multithreaded semi-global matching with an arbitrary number of path directions
Corresponding paper: LOCK-FREE MULTITHREADED SEMI-GLOBAL MATCHING WITH AN ARBITRARY NUMBER OF PATH DIRECTIONS
Auhor(s): D. Frommholz
Volume: Annals V-2-2020 / 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-143-2020

Mitigating image residuals systematic patterns in underwater photogrammetry
Corresponding paper: MITIGATING IMAGE RESIDUALS SYSTEMATIC PATTERNS IN UNDERWATER PHOTOGRAMMETRY
Auhor(s): F. Menna, E. Nocerino, S. Ural, and A. Gruen
Volume: Archives XLIII-B2-2020 / 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-977-2020

Learning with real-world and artificial data for improved vehicle detection in aerial imagery
Corresponding paper: LEARNING WITH REAL-WORLD AND ARTIFICIAL DATA FOR IMPROVED VEHICLE DETECTION IN AERIAL IMAGERY
Auhor(s): I. Weber, J. Bongartz, and R. Roscher
Volume: Annals V-2-2020 / 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-917-2020

Improving disparity estimation based on residual cost volume and reconstruction error volume
Corresponding paper: IMPROVING DISPARITY ESTIMATION BASED ON RESIDUAL COST VOLUME AND RECONSTRUCTION ERROR VOLUME
Auhor(s): J. Kang, L. Chen, F. Deng, and C. Heipke
Volume: Archives XLIII-B2-2020 / 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-135-2020

Dense matching comparison between classical and deep learning based algorithms for remote sensing data
Corresponding paper: DENSE MATCHING COMPARISON BETWEEN CLASSICAL AND DEEP LEARNING BASED ALGORITHMS FOR REMOTE SENSING DATA
Auhor(s): Y. Xia, P. d’Angelo, J. Tian, and P. Reinartz
Volume: Archives XLIII-B2-2020 / 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-521-2020