Dispersed Structured Light for Hyperspectral 3D Imaging

Suhyun Shin, Seokjun Choi, Felix Heide, Seung Hwan Baek

Research output: Contribution to journalConference articlepeer-review

Abstract

Hyperspectral 3D imaging aims to acquire both depth and spectral information of a scene. However, existing methods are either prohibitively expensive and bulky or compromise on spectral and depth accuracy. In this paper, we present Dispersed Structured Light (DSL), a cost-effective and compact method for accurate hyperspectral 3D imaging. DSL modifies a traditional projectorcamera system by placing a sub-millimeter thick diffraction grating film front of the projector. This configuration enables dispersing structured light based on light wavelength. To utilize the dispersed structured light, we devise a model for dispersive projection image formation and a per-pixel hyperspectral 3D reconstruction method. We validate DSL by instantiating a compact experimental prototype. DSL achieves spectral accuracy of 18.8 nm full-width half-maximum (FWHM) and depth error of 1 mm, outperforming prior work on practical hyperspectral 3D imaging. DSL promises accurate and practical hyperspectral 3D imaging for diverse application domains, including computer vision and graphics, cultural heritage, geology, and biology.

Original languageEnglish (US)
Pages (from-to)24997-25006
Number of pages10
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
StatePublished - 2024
Event2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 - Seattle, United States
Duration: Jun 16 2024Jun 22 2024

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Keywords

  • Hyperspectral 3D Imaging

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