# DASC: Robust Dense Descriptor for Multi-modal and Multi-spectral Correspondence Estimation

arXiv: https://arxiv.org/abs/1604.07944 · HTML: https://mlsanity.com/paper/1604.07944/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2016.

## Built on by (most cited first, 10 of 10)

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- [Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration](https://mlsanity.com/paper/2304.05646/) (2023)
- [Representing Noisy Image Without Denoising](https://mlsanity.com/paper/2301.07409/) (2023)
- [Towards RGB-NIR Cross-modality Image Registration and Beyond](https://mlsanity.com/paper/2405.19914/) (2024)
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- [HOT-POT: Optimal Transport for Sparse Stereo Matching](https://mlsanity.com/paper/2601.12423/) (2026)
- [XCalib Depth-Guided Geometric Optimization for Dense Thermal-Visible Video Registration](https://mlsanity.com/paper/2609.21770/) (2026)

## Abstract (arXiv)

Establishing dense correspondences between multiple images is a fundamental task in many applications. However, finding a reliable correspondence in multi-modal or multi-spectral images still remains unsolved due to their challenging photometric and geometric variations. In this paper, we propose a novel dense descriptor, called dense adaptive self-correlation (DASC), to estimate multi-modal and multi-spectral dense correspondences. Based on an observation that self-similarity existing within images is robust to imaging modality variations, we define the descriptor with a series of an adaptive self-correlation similarity measure between patches sampled by a randomized receptive field pooling, in which a sampling pattern is obtained using a discriminative learning. The computational redundancy of dense descriptors is dramatically reduced by applying fast edge-aware filtering. Furthermore, in order to address geometric variations including scale and rotation, we propose a geometry-invariant DASC (GI-DASC) descriptor that effectively leverages the DASC through a superpixel-based representation. For a quantitative evaluation of the GI-DASC, we build a novel multi-modal benchmark as varying photometric and geometric conditions. Experimental results demonstrate the outstanding performance of the DASC and GI-DASC in many cases of multi-modal and multi-spectral dense correspondences.