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

Sier Ha, Honghao Du, Xianjia Yu, Jian Song, Tomi Westerlund

TL;DR

This work addresses the challenge of processing low-resolution, dark lidar-derived images by surveying DL-based colorization and super-resolution methods adapted to lidar data. It examines a broad set of colorization and SR models, providing qualitative evaluations on indoor and outdoor lidar imagery and reporting laptop-based runtime benchmarks to assess real-time viability. The study highlights practical insights for deploying these techniques to improve lidar-image fusion, odometry, and 3D reconstruction, and presents preliminary evidence that SR/colorization can reduce false detections in downstream perception systems. Overall, the paper offers a comprehensive, implementation-focused assessment that informs model selection for robotic and autonomous systems operating under challenging sensing conditions.

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.

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

TL;DR

This work addresses the challenge of processing low-resolution, dark lidar-derived images by surveying DL-based colorization and super-resolution methods adapted to lidar data. It examines a broad set of colorization and SR models, providing qualitative evaluations on indoor and outdoor lidar imagery and reporting laptop-based runtime benchmarks to assess real-time viability. The study highlights practical insights for deploying these techniques to improve lidar-image fusion, odometry, and 3D reconstruction, and presents preliminary evidence that SR/colorization can reduce false detections in downstream perception systems. Overall, the paper offers a comprehensive, implementation-focused assessment that informs model selection for robotic and autonomous systems operating under challenging sensing conditions.

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.
Paper Structure (13 sections, 3 figures, 4 tables)

This paper contains 13 sections, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. DL based super-resolution and colorization
  3. DL-based Colorization
  4. DL-based Super Resolution
  5. Evaluation on lidar imagery
  6. Dataset for Evaluation
  7. Analysis of Colorization Approaches
  8. Analysis of Super-Resolution Approaches
  9. Hardware Information
  10. Experimental Results
  11. Super-Resolution of lidar imagery
  12. Colorization of LiDAR imagery
  13. Conclusion and Future Work

Figures (3)

  • Figure 1: DL-based super-resolution and colorization results for lidar image: RGB (left), lidar signal, colorized near-IR, and colorized signal images (right, top to bottom)
  • Figure 2: Raw images of indoor and outdoor environment
  • Figure 4: Instance segmentation results for different lidar image types. Each image type (subfigure \ref{['fig:seg_lidar_image_nir']} or \ref{['fig:seg_lidar_image_reflect']}) shows: original (top-left), result on original (top-right), after CRAN ahn2018fast super-resolution (bottom-left), and after super-resolution and Deoldify antic2019deoldify colorization (bottom-right).