分析深度学习在激光雷达图像色彩化与超分辨率中的应用与性能。
Analysis of Deep Learning-Based Colorization and Super-Resolution Techniques for Lidar Imagery
- 系统梳理了用于激光雷达图像的深度学习色彩化与超分辨率方法。
- 评估了各类方法在计算效率上的表现，适配机器人导航等实时场景。
- 揭示了低光照下激光雷达图像增强的技术潜力，适合自动驾驶系统使用。
现代激光雷达系统不仅能生成密集点云，还能提供360度的低分辨率图像。这一进展使原本为传统RGB相机设计的深度学习技术得以直接应用于激光雷达图像，无需复杂的激光雷达-相机标定过程。相比传统相机的RGB图像，激光雷达生成的图像在低光和恶劣天气（如雾天）条件下更具鲁棒性。然而，这些图像通常分辨率较低且过暗。尽管已有研究将深度学习应用于激光雷达图像的目标检测、分割和关键点识别，但其他潜在有价值的处理技术仍未被充分探索。本文对基于深度学习的激光雷达图像色彩化与超分辨率方法进行了全面综述与定性分析，并评估了这些方法的计算性能，为里程计、三维重建等下游机器人与自动驾驶系统应用提供了可行性参考。
原文摘要 · Abstract (English)
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.
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