Towards learning monocular 3D object localization from 2D labels using the physical laws of motion

  • We present a novel method for precise 3D object localization in single images from a single calibrated camera using only 2D labels. No expensive 3D labels are needed. Thus, instead of using 3D labels, our model is trained with easy-to-annotate 2D labels along with the physical knowledge of the object’s motion. Given this information, the model can infer the latent third dimension, even though it has never seen this information during training. Our method is evaluated on both synthetic and real-world datasets, and we are able to achieve a mean distance error of just 6 cm in our experiments on real data. The results indicate the method’s potential as a step towards learning 3D object location estimation, where collecting 3D data for training is not feasible.

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Author:Daniel KienzleGND, Katja LudwigGND, Julian LorenzGND, Rainer LienhartGND
Frontdoor URL
Parent Title (English):International Conference on 3D Vision 2024, March 18-21, 2024, Davos, Switzerland
Place of publication:Piscataway, NJ
Editor:Theodora Kontogianni, Akihiro Sugimoto, Gopal Sharma
Type:Conference Proceeding
Year of first Publication:2024
Publishing Institution:Universität Augsburg
Release Date:2024/01/25
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Maschinelles Lernen und Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Latest Publications (not yet published in print):Aktuelle Publikationen (noch nicht gedruckt erschienen)
Licence (German):Deutsches Urheberrecht