# Spectral 3D Computer Vision -- A Review

arXiv: https://arxiv.org/abs/2302.08054 · HTML: https://mlsanity.com/paper/2302.08054/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 52 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [Hyperspectral pansharpening: a review](https://mlsanity.com/paper/1504.04531/) (2015)
- [Pro-Cam SSfM: Projector-Camera System for Structure and Spectral Reflectance from Motion](https://mlsanity.com/paper/1908.08185/) (2019)
- [Spectral MVIR: Joint Reconstruction of 3D Shape and Spectral Reflectance](https://mlsanity.com/paper/2104.07308/) (2021)
- [Hyperspectral 3D Mapping of Underwater Environments](https://mlsanity.com/paper/2110.06571/) (2021)

## Built on by (most cited first, 1 of 1)

- [Spec-NeRF: Multi-spectral Neural Radiance Fields](https://mlsanity.com/paper/2310.12987/) (2023)

## Abstract (arXiv)

Spectral 3D computer vision examines both the geometric and spectral properties of objects. It provides a deeper understanding of an object's physical properties by providing information from narrow bands in various regions of the electromagnetic spectrum. Mapping the spectral information onto the 3D model reveals changes in the spectra-structure space or enhances 3D representations with properties such as reflectance, chromatic aberration, and varying defocus blur. This emerging paradigm advances traditional computer vision and opens new avenues of research in 3D structure, depth estimation, motion analysis, and more. It has found applications in areas such as smart agriculture, environment monitoring, building inspection, geological exploration, and digital cultural heritage records. This survey offers a comprehensive overview of spectral 3D computer vision, including a unified taxonomy of methods, key application areas, and future challenges and prospects.