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Conference proceeding
Friction from Reflectance: Deep Reflectance Codes for Predicting Physical Surface Properties from One-Shot In-Field Reflectance
COMPUTER VISION - ECCV 2016, PT IV, v 9908, pp 808-824
01 Jan 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Images are the standard input for vision algorithms, but one-shot in-field reflectance measurements are creating new opportunities for recognition and scene understanding. In this work, we address the question of what reflectance can reveal about materials in an efficient manner. We go beyond the question of recognition and labeling and ask the question: What intrinsic physical properties of the surface can be estimated using reflectance? We introduce a framework that enables prediction of actual friction values for surfaces using one-shot reflectance measurements. This work is a first of its kind vision-based friction estimation. We develop a novel representation for reflectance disks that capture partial BRDF measurements instantaneously. Our method of deep reflectance codes combines CNN features and fisher vector pooling with optimal binary embedding to create codes that have sufficient discriminatory power and have important properties of illumination and spatial invariance. The experimental results demonstrate that reflectance can play a new role in deciphering the underlying physical properties of real-world scenes.
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Details
- Title
- Friction from Reflectance: Deep Reflectance Codes for Predicting Physical Surface Properties from One-Shot In-Field Reflectance
- Creators
- Hang Zhang - Rutgers, The State University of New JerseyKristin Dana - Rutgers, The State University of New JerseyKo Nishino - Drexel University
- Contributors
- B Leibe (Editor)J Matas (Editor)N Sebe (Editor)M Welling (Editor)
- Publication Details
- COMPUTER VISION - ECCV 2016, PT IV, v 9908, pp 808-824
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 17
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000389385100049
- Scopus ID
- 2-s2.0-84990050144
- Other Identifier
- 991019173562704721
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Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods
- Imaging Science & Photographic Technology