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Continuous Optimization Framework for Depth Sensor Viewpoint Selection

EasyChair Preprint no. 749

17 pagesPublished: January 22, 2019

Abstract

Distinguishing differences between areas represented with point cloud data is generally approached by choosing a single best viewpoint. The most informative view of a scene ultimately enables to have the best coverage over distinct points both locally and globally while accounting for the distance to the foci of attention. Measures of surface saliency, related to curvature inconsistency, extenuate differences in shape and are coupled with viewpoint selection approaches. As there is no analytical solution for optimal viewpoint selection, candidate viewpoints are generally discretely sampled and evaluated for information and require (near) exhaustive combinatorial searches. We present a consolidated optimization framework for best viewpoint selection with a continuous cost function and analytically derived Jacobian that incorporates view angle, vertex normals and surface quality relative to viewpoint. We provide a mechanism in the cost function to incorporate sensor attributes such as operating range, field of view and angular resolution. The framework is evaluated as competing favourably with the state-of-the-art approaches to viewpoint selection while significantly reducing the number of viewpoints to be evaluated in the process.

Keyphrases: depth sensors, Optimal Viewpoint, perception

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:749,
  author = {Behnam Maleki and Alen Alempijevic and Teresa Vidal-Calleja},
  title = {Continuous Optimization Framework for Depth Sensor Viewpoint Selection},
  howpublished = {EasyChair Preprint no. 749},
  doi = {10.29007/6j25},
  year = {EasyChair, 2019}}
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