LiDAR Point Cloud Colourisation Using Multi-Camera Fusion and Low-Light Image Enhancement

2025-09-30

AI Summary

To address the challenges of colorization, chromatic distortion, and detail loss in mechanical LiDAR point clouds under low-light conditions, this paper proposes a tightly coupled multi-camera–LiDAR fusion framework. Methodologically, it constructs a 360° panoramic system comprising four synchronized cameras, integrating intrinsic calibration, automatic extrinsic estimation (target-free), inter-camera color consistency correction, and an embedded low-light image enhancement module; enhanced high-fidelity color images are then precisely projected onto Velodyne Puck Hi-Res point clouds. The key contribution is the first end-to-end integration of a deep low-light enhancement model into the sensor fusion pipeline, significantly improving color fidelity and texture discernibility in extremely dark scenes (<0.1 lux). Additionally, the framework achieves fully automatic geometric registration and color correction, enabling plug-and-play deployment. Experimental results demonstrate robust, high-fidelity, omnidirectional colored point cloud generation at near-real-time performance.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

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

Abstract

In recent years, the fusion of camera data with LiDAR measurements has emerged as a powerful approach to enhance spatial understanding. This study introduces a novel, hardware-agnostic methodology that generates colourised point clouds from mechanical LiDAR using multiple camera inputs, providing complete 360-degree coverage. The primary innovation lies in its robustness under low-light conditions, achieved through the integration of a low-light image enhancement module within the fusion pipeline. The system requires initial calibration to determine intrinsic camera parameters, followed by automatic computation of the geometric transformation between the LiDAR and cameras, removing the need for specialised calibration targets and streamlining the setup. The data processing framework uses colour correction to ensure uniformity across camera feeds before fusion. The algorithm was tested using a Velodyne Puck Hi-Res LiDAR and a four-camera configuration. The optimised software achieved real-time performance and reliable colourisation even under very low illumination, successfully recovering scene details that would otherwise remain undetectable.

Problem

• Colorizing LiDAR point clouds using multi-camera fusion
• Enhancing robustness under low-light conditions
• Achieving real-time performance without specialized calibration targets

Innovation

• Multi-camera fusion for 360-degree colored point clouds
• Low-light enhancement module integrated in fusion pipeline
• Automatic geometric calibration without specialized targets