Publication
Safari from visual signals: Recovering volumetric 3D shapes
Conference Article
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Edition
2022
Pages
2495-2499
Doc link
http://dx.doi.org/10.1109/ICASSP43922.2022.9746343
File
Abstract
In this paper we propose a convex approach for recovering a detailed 3D volumetric geometry of several objects from visual signals. To this end, we first present a minimal detailed surface energy that is optimized together with a volume constraint by considering some geometrical priors, and without requiring neither additional training data nor templates in order to constrain the solution. Our problem can be efficiently solved by means of a gradient descent, and be applied for single RGB images or monocular videos even with very small rigid motions. Temporal-aware solutions and driven by point correspondences are incorporated without assuming any 2D tracking data over time. Thanks to this formulation, both rigid and non-rigid objects can be considered. We have extensively validated our approach in a wide variety of scenarios in the wild, recovering challenging type of shapes that have not been previously attempted without assuming any training data.
Categories
computer vision.
Author keywords
Visual Signals, Mininal Surfaces, Image and Video Understanding
Scientific reference
A. Agudo. Safari from visual signals: Recovering volumetric 3D shapes, 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, 2022, Singapore, pp. 2495-2499.
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