Deep Hyperspectral-Depth Reconstruction
Using Single Color-Dot Projection

Chunyu Li, Yusuke Monno, and Masatoshi Okutomi
Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology
Conference on Computer Vision and Pattern Recognition (CVPR2022)

Abstract

Depth reconstruction and hyperspectral reflectance reconstruction are two active research topics in computer vision and image processing. Conventionally, these two topics have been studied separately using independent imaging setups and there is no existing method which can acquire depth and spectral reflectance simultaneously in one shot without using special hardware. In this paper, we propose a novel single-shot hyperspectral-depth reconstruction method using an off-the-shelf RGB camera and projector. Our method is based on a single color-dot projection, which simultaneously acts as structured light for depth reconstruction and spatially-varying color illuminations for hyperspectral reflectance reconstruction. To jointly reconstruct the depth and the hyperspectral reflectance from a single color-dot image, we propose a novel end-to-end network architecture that effectively incorporates a geometric color-dot pattern loss and a photometric hyperspectral reflectance loss. Through the experiments, we demonstrate that our hyperspectral-depth reconstruction method outperforms the combination of an existing state-of-the-art single-shot hyperspectral reflectance reconstruction method and depth reconstruction method.

CVPR 2022 Overview
Overview

Results

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Dynamic Scene

Publication

Deep Hyperspectral-Depth Reconstruction Using Single Color-Dot Projection

Authers: Chunyu Li, Yusuke Monno, and Masatoshi Okutomi
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR2022), June, 2022


@article{li2022,
	  title={Deep Hyperspectral-Depth Reconstruction Using Single Color-Dot Projection,
	  author={Li, Chunyu and Monno, Yusuke and and Okutomi, Masatoshi},
	  journal={Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
	  year={2022}
	}