# Dense Dispersed Structured Light for Hyperspectral 3D Imaging of Dynamic Scenes

arXiv: https://arxiv.org/abs/2412.01140 · HTML: https://mlsanity.com/paper/2412.01140/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Optics](https://mlsanity.com/paper/2009.00463/) (2020)
- [ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems](https://mlsanity.com/paper/1807.06009/) (2018)
- [Polka Lines: Learning Structured Illumination and Reconstruction for Active Stereo](https://mlsanity.com/paper/2011.13117/) (2020)
- [Pro-Cam SSfM: Projector-Camera System for Structure and Spectral Reflectance from Motion](https://mlsanity.com/paper/1908.08185/) (2019)
- [Dispersed Structured Light for Hyperspectral 3D Imaging](https://mlsanity.com/paper/2311.18287/) (2023)
- [Fast and robust pushbroom hyperspectral imaging via DMD-based scanning](https://mlsanity.com/paper/1608.00361/) (2016)

## Built on by (most cited first, 3 of 3)

- [Towards Imperceptible Watermarking Via Environment Illumination for Consumer Cameras](https://mlsanity.com/paper/2510.17114/) (2025)
- [Lumosaic: Hyperspectral Video via Active Illumination and Coded-Exposure Pixels](https://mlsanity.com/paper/2602.22140/) (2026)
- [Broadband Hyperspectral 3D Imaging using Dispersed Structured Light](https://mlsanity.com/paper/2605.25757/) (2026)

## Abstract (arXiv)

Hyperspectral 3D imaging captures both depth maps and hyperspectral images, enabling comprehensive geometric and material analysis. Recent methods achieve high spectral and depth accuracy; however, they require long acquisition times often over several minutes or rely on large, expensive systems, restricting their use to static scenes. We present Dense Dispersed Structured Light (DDSL), an accurate hyperspectral 3D imaging method for dynamic scenes that utilizes stereo RGB cameras and an RGB projector equipped with an affordable diffraction grating film. We design spectrally multiplexed DDSL patterns that significantly reduce the number of required projector patterns, thereby accelerating acquisition speed. Additionally, we formulate an image formation model and a reconstruction method to estimate a hyperspectral image and depth map from captured stereo images. As the first practical and accurate hyperspectral 3D imaging method for dynamic scenes, we experimentally demonstrate that DDSL achieves a spectral resolution of 15.5 nm full width at half maximum (FWHM), a depth error of 4 mm, and a frame rate of 6.6 fps.