# FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving

arXiv: https://arxiv.org/abs/1910.05395 · HTML: https://mlsanity.com/paper/1910.05395/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 26 of 26)

- [RGB-Event Fusion for Moving Object Detection in Autonomous Driving](https://mlsanity.com/paper/2209.08323/) (2022)
- [Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving](https://mlsanity.com/paper/2310.07602/) (2023)
- [Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2103.17001/) (2021)
- [Video Class Agnostic Segmentation Benchmark for Autonomous Driving](https://mlsanity.com/paper/2103.11015/) (2021)
- [Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event Camera](https://mlsanity.com/paper/2309.09297/) (2023)
- [LightSim: Neural Lighting Simulation for Urban Scenes](https://mlsanity.com/paper/2312.06654/) (2023)
- [4D Unsupervised Object Discovery](https://mlsanity.com/paper/2210.04801/) (2022)
- [BEVDetNet: Bird's Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2104.10780/) (2021)
- [LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation](https://mlsanity.com/paper/2111.04875/) (2021)
- [PSENet: Progressive Self-Enhancement Network for Unsupervised Extreme-Light Image Enhancement](https://mlsanity.com/paper/2210.00712/) (2022)
- [Spatio-Temporal Multi-Task Learning Transformer for Joint Moving Object Detection and Segmentation](https://mlsanity.com/paper/2106.11401/) (2021)
- [Optical Flow for Autonomous Driving: Applications, Challenges and Improvements](https://mlsanity.com/paper/2301.04422/) (2023)
- [BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving](https://mlsanity.com/paper/2107.04937/) (2021)
- [FlowLUT: Efficient Image Enhancement via Differentiable LUTs and Iterative Flow Matching](https://mlsanity.com/paper/2509.23608/) (2025)
- [RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions](https://mlsanity.com/paper/2109.01820/) (2021)
- [Weakly Supervised Instance Segmentation using Motion Information via Optical Flow](https://mlsanity.com/paper/2202.13006/) (2022)
- [Low-Light Enhancement in the Frequency Domain](https://mlsanity.com/paper/2306.16782/) (2023)
- [Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions](https://mlsanity.com/paper/2404.11214/) (2024)
- [BRIGHT-VO: Brightness-Guided Hybrid Transformer for Visual Odometry with Multi-modality Refinement Module](https://mlsanity.com/paper/2501.08659/) (2025)
- [Channel-wise Motion Features for Efficient Motion Segmentation](https://mlsanity.com/paper/2507.13082/) (2025)
- [Meta Fusion: A Unified Framework For Multimodality Fusion with Mutual Learning](https://mlsanity.com/paper/2507.20089/) (2025)
- [VM-MODNet: Vehicle Motion aware Moving Object Detection for Autonomous Driving](https://mlsanity.com/paper/2104.10985/) (2021)
- [Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems](https://mlsanity.com/paper/2105.10316/) (2021)
- [ST-DETR: Spatio-Temporal Object Traces Attention Detection Transformer](https://mlsanity.com/paper/2107.05887/) (2021)
- [Leveraging Multi-stream Information Fusion for Trajectory Prediction in Low-illumination Scenarios: A Multi-channel Graph Convolutional Approach](https://mlsanity.com/paper/2211.10226/) (2022)
- [Low-light Pedestrian Detection in Visible and Infrared Image Feeds: Issues and Challenges](https://mlsanity.com/paper/2311.08557/) (2023)

## Abstract (arXiv)

Moving object detection is a critical task for autonomous vehicles. As dynamic objects represent higher collision risk than static ones, our own ego-trajectories have to be planned attending to the future states of the moving elements of the scene. Motion can be perceived using temporal information such as optical flow. Conventional optical flow computation is based on camera sensors only, which makes it prone to failure in conditions with low illumination. On the other hand, LiDAR sensors are independent of illumination, as they measure the time-of-flight of their own emitted lasers. In this work, we propose a robust and real-time CNN architecture for Moving Object Detection (MOD) under low-light conditions by capturing motion information from both camera and LiDAR sensors. We demonstrate the impact of our algorithm on KITTI dataset where we simulate a low-light environment creating a novel dataset "Dark KITTI". We obtain a 10.1% relative improvement on Dark-KITTI, and a 4.25% improvement on standard KITTI relative to our baselines. The proposed algorithm runs at 18 fps on a standard desktop GPU using 256×1224 resolution images.