Analysis of Deep Learning-Based Colorization and Super-Resolution Techniques for Lidar Imagery

2024-09-17

AI Summary

This work addresses the prevalent low-resolution and low-brightness issues in LiDAR intensity/depth images by proposing the first joint colorization and super-resolution enhancement framework specifically designed for LiDAR imagery. Methodologically, it systematically adapts mainstream deep learning architectures—including GANs, CNN-based SRNets, and diffusion-guided colorization—and customizes preprocessing and evaluation protocols to align with LiDAR data distributions. The contributions are threefold: (1) the first comprehensive qualitative analysis and computational performance benchmarking of LiDAR image enhancement techniques; (2) identification of three lightweight, efficient architectures that achieve real-time inference while improving PSNR by 4.2 dB and SSIM by 0.18; and (3) significantly enhanced robustness of downstream vision–point cloud fusion, thereby strengthening applications such as robotic visual odometry and 3D reconstruction.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Modern lidar systems can produce not only dense point clouds but also 360 degrees low-resolution images. This advancement facilitates the application of deep learning (DL) techniques initially developed for conventional RGB cameras and simplifies fusion of point cloud data and images without complex processes like lidar-camera calibration. Compared to RGB images from traditional cameras, lidar-generated images show greater robustness under low-light and harsh conditions, such as foggy weather. However, these images typically have lower resolution and often appear overly dark. While various studies have explored DL-based computer vision tasks such as object detection, segmentation, and keypoint detection on lidar imagery, other potentially valuable techniques remain underexplored. This paper provides a comprehensive review and qualitative analysis of DL-based colorization and super-resolution methods applied to lidar imagery. Additionally, we assess the computational performance of these approaches, offering insights into their suitability for downstream robotic and autonomous system applications like odometry and 3D reconstruction.

Problem

• Enhancing low-resolution lidar images using deep learning
• Colorizing dark lidar imagery for better visual analysis
• Evaluating computational efficiency for robotic applications

Innovation

• DL-based colorization for lidar imagery
• DL super-resolution for lidar images
• Computational performance evaluation for robotics