# Keypoint Detection and Description for Raw Bayer Images

arXiv: https://arxiv.org/abs/2503.08673 · HTML: https://mlsanity.com/paper/2503.08673/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [DISK: Learning local features with policy gradient](https://mlsanity.com/paper/2006.13566/) (2020)
- [LIFT: Learned Invariant Feature Transform](https://mlsanity.com/paper/1603.09114/) (2016)
- [HPatches: A benchmark and evaluation of handcrafted and learned local descriptors](https://mlsanity.com/paper/1704.05939/) (2017)
- [Working hard to know your neighbor's margins: Local descriptor learning loss](https://mlsanity.com/paper/1705.10872/) (2017)
- [LF-Net: Learning Local Features from Images](https://mlsanity.com/paper/1805.09662/) (2018)
- [ASLFeat: Learning Local Features of Accurate Shape and Localization](https://mlsanity.com/paper/2003.10071/) (2020)
- [ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation](https://mlsanity.com/paper/2304.03608/) (2023)
- [ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction](https://mlsanity.com/paper/2112.02906/) (2021)
- [Repeatability Is Not Enough: Learning Affine Regions via Discriminability](https://mlsanity.com/paper/1711.06704/) (2017)
- [Image Restoration for Under-Display Camera](https://mlsanity.com/paper/2003.04857/) (2020)
- [D2D: Keypoint Extraction with Describe to Detect Approach](https://mlsanity.com/paper/2005.13605/) (2020)

## Built on by (most cited first, 4 of 4)

- [Adaptive Event Stream Slicing for Open-Vocabulary Event-Based Object Detection via Vision-Language Knowledge Distillation](https://mlsanity.com/paper/2510.00681/) (2025)
- [3D Plant Root Skeleton Detection and Extraction](https://mlsanity.com/paper/2508.08094/) (2025)
- [Foundation-Assisted Active Learning for Object Detection Annotation](https://mlsanity.com/paper/2607.16671/) (2026)
- [Multimodal Plant Root Phenotyping with Integration of 3D Skeleton Extraction and Language Analysis](https://mlsanity.com/paper/2608.03109/) (2026)

## Abstract (arXiv)

Keypoint detection and local feature description are fundamental tasks in robotic perception, critical for applications such as SLAM, robot localization, feature matching, pose estimation, and 3D mapping. While existing methods predominantly operate on RGB images, we propose a novel network that directly processes raw images, bypassing the need for the Image Signal Processor (ISP). This approach significantly reduces hardware requirements and memory consumption, which is crucial for robotic vision systems. Our method introduces two custom-designed convolutional kernels capable of performing convolutions directly on raw images, preserving inter-channel information without converting to RGB. Experimental results show that our network outperforms existing algorithms on raw images, achieving higher accuracy and stability under large rotations and scale variations. This work represents the first attempt to develop a keypoint detection and feature description network specifically for raw images, offering a more efficient solution for resource-constrained environments.