HIGH-FREQUENCY SHAPE RECOVERY FROM SHADING BY CNN AND DOMAIN ADAPTATION

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Importance of structured-light based one-shot scanning technique is increasing because of its simple system configuration and ability of capturing moving objects. One severe limitation of the technique is that it can capture only sparse shape, but not high frequency shapes, because certain area of projection pattern is required to encode spatial information. In this paper, we propose a technique to recover high-frequency shapes by using shading information, which is captured by one-shot RGB-D sensor based on structured light with single camera. Since color image comprises shading information of object surface, high-frequency shapes can be recovered by shape from shading techniques. Although multiple images with different lighting positions are required for shape from shading techniques, we propose a learning based approach to recover shape from a single image. In addition, to overcome the problem of preparing sufficient amount of data for training, we propose a new data augmentation method for high-frequency shapes using synthetic data and domain adaptation. Experimental results are shown to confirm the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PublisherIEEE Computer Society
Pages3672-3676
Number of pages5
ISBN (Electronic)9781665441155
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: Sep 19 2021Sep 22 2021

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2021-September
ISSN (Print)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States
CityAnchorage
Period9/19/219/22/21

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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