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

arXiv: https://arxiv.org/abs/2509.25859 · HTML: https://mlsanity.com/paper/2509.25859/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [A General and Adaptive Robust Loss Function](https://mlsanity.com/paper/1701.03077/) (2017)
- [Getting to Know Low-light Images with The Exclusively Dark Dataset](https://mlsanity.com/paper/1805.11227/) (2018)
- [Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review](https://mlsanity.com/paper/2004.05224/) (2020)
- [General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox](https://mlsanity.com/paper/2302.05094/) (2023)
- [Joint Camera Intrinsic and LiDAR-Camera Extrinsic Calibration](https://mlsanity.com/paper/2202.13708/) (2022)
- [Point Cloud Colorization Based on Densely Annotated 3D Shape Dataset](https://mlsanity.com/paper/1810.05396/) (2018)
- [Vector Quantized Bayesian Neural Network Inference for Data Streams](https://mlsanity.com/paper/1907.05911/) (2019)
- [OmniColor: A Global Camera Pose Optimization Approach of LiDAR-360Camera Fusion for Colorizing Point Clouds](https://mlsanity.com/paper/2404.04693/) (2024)
- [Convolutional Neural Network for Blur Images Detection as an Alternative for Laplacian Method](https://mlsanity.com/paper/2010.07936/) (2020)
- [Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy](https://mlsanity.com/paper/2410.06725/) (2024)
- [Galibr: Targetless LiDAR-Camera Extrinsic Calibration Method via Ground Plane Initialization](https://mlsanity.com/paper/2406.11599/) (2024)
- [3D Reconstruction via Incremental Structure From Motion](https://mlsanity.com/paper/2508.01019/) (2025)

## Abstract (arXiv)

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.