# LIBRE: The Multiple 3D LiDAR Dataset

arXiv: https://arxiv.org/abs/2003.06129 · HTML: https://mlsanity.com/paper/2003.06129/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

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

- [Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving](https://mlsanity.com/paper/2303.11040/) (2023)
- [Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather](https://mlsanity.com/paper/2108.05249/) (2021)
- [LiDAR Snowfall Simulation for Robust 3D Object Detection](https://mlsanity.com/paper/2203.15118/) (2022)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [A Survey on Deep Domain Adaptation for LiDAR Perception](https://mlsanity.com/paper/2106.02377/) (2021)
- [Survey on LiDAR Perception in Adverse Weather Conditions](https://mlsanity.com/paper/2304.06312/) (2023)
- [Instant Domain Augmentation for LiDAR Semantic Segmentation](https://mlsanity.com/paper/2303.14378/) (2023)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [A Survey of Robust 3D Object Detection Methods in Point Clouds](https://mlsanity.com/paper/2204.00106/) (2022)
- [Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning](https://mlsanity.com/paper/2102.04960/) (2021)
- [Analyzing Deep Learning Representations of Point Clouds for Real-Time In-Vehicle LiDAR Perception](https://mlsanity.com/paper/2210.14612/) (2022)
- [Cross-spectral Gated-RGB Stereo Depth Estimation](https://mlsanity.com/paper/2405.12759/) (2024)
- [Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues](https://mlsanity.com/paper/2305.12955/) (2023)
- [Polarization Wavefront Lidar: Learning Large Scene Reconstruction from Polarized Wavefronts](https://mlsanity.com/paper/2406.03461/) (2024)
- [TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather](https://mlsanity.com/paper/2408.13802/) (2024)
- [Gated2Gated: Self-Supervised Depth Estimation from Gated Images](https://mlsanity.com/paper/2112.02416/) (2021)
- [Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Design](https://mlsanity.com/paper/2104.06615/) (2021)
- [VIFNet: An End-to-end Visible-Infrared Fusion Network for Image Dehazing](https://mlsanity.com/paper/2404.07790/) (2024)
- [HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors](https://mlsanity.com/paper/2408.06328/) (2024)
- [Predictive World Models from Real-World Partial Observations](https://mlsanity.com/paper/2301.04783/) (2023)
- [Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective](https://mlsanity.com/paper/2402.02738/) (2024)
- [Gated Fields: Learning Scene Reconstruction from Gated Videos](https://mlsanity.com/paper/2405.19819/) (2024)
- [APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point Clouds](https://mlsanity.com/paper/2505.09971/) (2025)
- [Analysis of LiDAR Configurations on Off-road Semantic Segmentation Performance](https://mlsanity.com/paper/2306.16551/) (2023)
- [Efficient Active Training for Deep LiDAR Odometry](https://mlsanity.com/paper/2509.03211/) (2025)
- [Semi-Supervised Diversity-Aware Domain Adaptation for 3D Object detection](https://mlsanity.com/paper/2512.24922/) (2025)

## Abstract (arXiv)

In this work, we present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors, covering a range of manufacturers, models, and laser configurations. Data captured independently from each sensor includes three different environments and configurations: static targets, where objects were placed at known distances and measured from a fixed position within a controlled environment; adverse weather, where static obstacles were measured from a moving vehicle, captured in a weather chamber where LiDARs were exposed to different conditions (fog, rain, strong light); and finally, dynamic traffic, where dynamic objects were captured from a vehicle driven on public urban roads, multiple times at different times of the day, and including supporting sensors such as cameras, infrared imaging, and odometry devices. LIBRE will contribute to the research community to (1) provide a means for a fair comparison of currently available LiDARs, and (2) facilitate the improvement of existing self-driving vehicles and robotics-related software, in terms of development and tuning of LiDAR-based perception algorithms.