Keypoint Detection and Description for Raw Bayer Images

2025-03-11

AI Summary

In resource-constrained robotic vision systems, conventional image signal processing (ISP) pipelines incur substantial hardware overhead and high memory consumption when converting raw Bayer images to RGB for downstream tasks. Method: This paper proposes, for the first time, an end-to-end keypoint detection and local feature description framework operating directly on raw Bayer data—bypassing ISP entirely. We design two specialized convolutional kernels that preserve the inherent channel structure of Bayer mosaics, enabling native raw-domain feature learning without RGB demosaicing. A lightweight network architecture is developed to support ISP-free processing. Contribution/Results: Experiments demonstrate significant improvements in keypoint detection accuracy and matching robustness under large viewpoint rotations and scale variations. Our method outperforms state-of-the-art RGB-domain approaches while reducing computational and memory footprint, making it highly suitable for embedded robotic vision applications.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Browser and end-point security

Abstract

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.

Problem

• Develops keypoint detection for raw Bayer images
• Eliminates need for Image Signal Processor (ISP)
• Reduces hardware and memory requirements

Innovation

• Direct raw image processing bypasses ISP
• Custom convolutional kernels preserve inter-channel info
• Higher accuracy in large rotations and scales