Learning Effective Sparse Sampling Strategies using Deep Active Sensing

Mehdi Patrick Stapleton, Dieter Schmalstieg, Clemens Arth, Thomas Gloor

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung


Registering a known model with noisy sample measurements is in general a difficult task due to the problem in finding correspondences between the samples and points on the known model. General frameworks exist, such as variants of the classical iterative closest point (ICP) method to iteratively refine correspondence estimates. However, the methods are prone to getting trapped in locally optimal configurations, which may be far from the true registration. The quality of the final registration depends strongly on the set of samples. The quality of the set of sample measurements is more noticeable when the number of samples is relatively low (≈ 20). We consider sample selection in the context of active perception, i.e. an objective-driven decision-making process, to motivate our research and the construction of our system. We design a system for learning how to select the regions of the scene to sample, and, in doing so, improve the accuracy and efficiency of the sampling process. We present a full environment for learning how best to sample a scene in order to quickly and accurately register a model with the scene. This work has broad applicability from the fields of geodesy to medical robotics, where the cost of taking a measurement is much higher than the cost of incremental changes to the pose of the equipment.

TitelVISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Redakteure/-innenGiovanni Maria Farinella, Petia Radeva, Jose Braz
Herausgeber (Verlag)SciTePress
ISBN (elektronisch)9789897584022
PublikationsstatusVeröffentlicht - Feb. 2020
Veranstaltung15th International Conference on Computer Vision Theory and Applications: VISAPP 2020 - Grand Hotel Excelsior, Valetta, Malta
Dauer: 27 Feb. 202029 Feb. 2020


Konferenz15th International Conference on Computer Vision Theory and Applications
KurztitelVISIGRAPP 2020

ASJC Scopus subject areas

  • Maschinelles Sehen und Mustererkennung
  • Angewandte Informatik
  • Computergrafik und computergestütztes Design


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