EE Seminar: Using artificial neural networks for Echo-based object classification & reconstruction
השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז. בצ'אט
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https://zoom.us/j/95381960872?pwd=bEVvNjYxOUNNRzN3Q20vWHY4SENTdz09
Meeting ID: 953 8196 0872
Password: 012254
Speaker: Netanel Frank
M.Sc. student under the supervision of Prof. Anthony Weiss and Prof. Yossi Yovel
Monday, May 4th, 2020 at 15:00
ZOOM
Using artificial neural networks for Echo-based object classification & reconstruction
Abstract
Our understanding of sonar-based sensing is very limited in comparison to light based imaging. In this work, we synthesize a ShapeNet variant in which echolocation replaces the role of vision. A new hypernetwork method is presented for 3D reconstruction from a single echolocation view. The success of the method demonstrates the ability to reconstruct a 3D shape from bat-like sonar, and not just obtain the relative position of the bat with respect to obstacles. In addition, it is shown that integrating information from multiple orientations around the same view point helps performance.
The sonar-based method we develop is analog to the state-of-the-art single image reconstruction method, which allows us to directly compare the two imaging modalities. Based on this analysis, we learn that while 3D can be reliably reconstructed form sonar, as far as the current technology shows, the accuracy is lower than the one obtained based on vision, that the performance in sonar and in vision are highly correlated, that both modalities favor shapes that are not round, and that while the current vision method is able to better reconstruct the 3D shape, its advantage with respect to estimating the normal's direction is much lower.
