Mine The Gap: Gap Estimation And Contact Detection Information Via Adjacent Surface Observation

Keywords

Contact detection; Depth sensing; Gap estimation; Indirect observation; Occlusion; SVM

Abstract

In general, conventional computer vision techniques suffer from an inability to detect hidden surface contacts due to line-of-sight visibility problems. Rather than fitting models to scene objects and estimating inter-object gaps, our approach is to leverage the fact that light passing between and reflecting off the surfaces can offer valuable information as it alters the appearance of nearby surfaces. For a proof of concept demonstration, we employed a machine learning approach to classifying adjacent surface imagery to estimate hidden surface distances and contact locations in a controlled setting under ambient lighting conditions. Our proof-of-concept results demonstrate relatively high accuracy for the estimation of gap size and the detection of contact between hidden surfaces. We envision such measures could someday provide complementary information to be combined with traditional visible-surface methods, to obtain more precise and robust estimates of hidden surface relationships.

Publication Date

8-15-2018

Publication Title

ACM International Conference Proceeding Series

Number of Pages

54-58

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/3243250.3243260

Socpus ID

85056728408 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85056728408

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