# Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping

arXiv: https://arxiv.org/abs/2111.11348 · HTML: https://mlsanity.com/paper/2111.11348/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 47 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [A2D2: Audi Autonomous Driving Dataset](https://mlsanity.com/paper/2004.06320/) (2020)
- [Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark](https://mlsanity.com/paper/1704.03847/) (2017)
- [SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances](https://mlsanity.com/paper/2002.09147/) (2020)
- [Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints](https://mlsanity.com/paper/1910.06727/) (2019)
- [Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges](https://mlsanity.com/paper/2009.03137/) (2020)
- [SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans](https://mlsanity.com/paper/1912.00036/) (2019)
- [Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways](https://mlsanity.com/paper/2003.08284/) (2020)
- [3D Semantic Scene Completion: a Survey](https://mlsanity.com/paper/2103.07466/) (2021)
- [DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation](https://mlsanity.com/paper/2004.11985/) (2020)
- [Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification](https://mlsanity.com/paper/1712.00032/) (2017)
- [IMLS-SLAM: scan-to-model matching based on 3D data](https://mlsanity.com/paper/1802.08633/) (2018)
- [SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans](https://mlsanity.com/paper/2006.14660/) (2020)
- [KITTI-CARLA: a KITTI-like dataset generated by CARLA Simulator](https://mlsanity.com/paper/2109.00892/) (2021)
- [Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception](https://mlsanity.com/paper/1905.00160/) (2019)
- [Review: deep learning on 3D point clouds](https://mlsanity.com/paper/2001.06280/) (2020)
- [Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene](https://mlsanity.com/paper/2008.04968/) (2020)
- [SynthCity: A large scale synthetic point cloud](https://mlsanity.com/paper/1907.04758/) (2019)
- [Torch-Points3D: A Modular Multi-Task Frameworkfor Reproducible Deep Learning on 3D Point Clouds](https://mlsanity.com/paper/2010.04642/) (2020)

## Built on by (most cited first, 25 of 25)

- [Synthesizing Diverse Human Motions in 3D Indoor Scenes](https://mlsanity.com/paper/2305.12411/) (2023)
- [MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments](https://mlsanity.com/paper/2203.07060/) (2022)
- [AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis](https://mlsanity.com/paper/2504.13157/) (2025)
- [PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows](https://mlsanity.com/paper/2203.05940/) (2022)
- [Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities](https://mlsanity.com/paper/2208.14167/) (2022)
- [MeshVPR: Citywide Visual Place Recognition Using 3D Meshes](https://mlsanity.com/paper/2406.02776/) (2024)
- [Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation](https://mlsanity.com/paper/2210.09739/) (2022)
- [Deep Learning on 3D Semantic Segmentation: A Detailed Review](https://mlsanity.com/paper/2411.02104/) (2024)
- [TUM-FAcCADE: Reviewing and enriching point cloud benchmarks for faccade segmentation](https://mlsanity.com/paper/2304.07140/) (2023)
- [TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset](https://mlsanity.com/paper/2505.07396/) (2025)
- [ZAHA: Introducing the Level of Facade Generalization and the Large-Scale Point Cloud Facade Semantic Segmentation Benchmark Dataset](https://mlsanity.com/paper/2411.04865/) (2024)
- [IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments](https://mlsanity.com/paper/2206.13455/) (2022)
- [Synth It Like KITTI: Synthetic Data Generation for Object Detection in Driving Scenarios](https://mlsanity.com/paper/2502.15076/) (2025)
- [TrueCity: Real and Simulated Urban Data for Cross-Domain 3D Scene Understanding](https://mlsanity.com/paper/2511.07007/) (2025)
- [Unsigned Distance Field as an Accurate 3D Scene Representation for Neural Scene Completion](https://mlsanity.com/paper/2203.09167/) (2022)
- [Mind the Domain Gap: Measuring the Domain Gap Between Real-World and Synthetic Point Clouds for Automated Driving Development](https://mlsanity.com/paper/2505.17959/) (2025)
- [PRISM: Color-Stratified Point Cloud Sampling](https://mlsanity.com/paper/2601.06839/) (2026)
- [UnderOneFacade: Worldwide Facade Semantic Segmentation Benchmark Dataset](https://mlsanity.com/paper/2607.02018/) (2026)
- [Simulation-to-Reality domain adaptation for offline 3D object annotation on pointclouds with correlation alignment](https://mlsanity.com/paper/2202.02666/) (2022)
- [Single-Shot Global Localization via Graph-Theoretic Correspondence Matching](https://mlsanity.com/paper/2306.03641/) (2023)
- [Randomized 3D Scene Generation for Generalizable Self-Supervised Pre-Training](https://mlsanity.com/paper/2306.04237/) (2023)
- [WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Research](https://mlsanity.com/paper/2402.19059/) (2024)
- [Omnidirectional Depth-Aided Occupancy Prediction based on Cylindrical Voxel for Autonomous Driving](https://mlsanity.com/paper/2504.01023/) (2025)
- [Text2Loc++: Generalizing 3D Point Cloud Localization from Natural Language](https://mlsanity.com/paper/2511.15308/) (2025)
- [AVOID: The Adverse Visual Conditions Dataset with Obstacles for Driving Scene Understanding](https://mlsanity.com/paper/2512.23215/) (2025)

## Abstract (arXiv)

Paris-CARLA-3D is a dataset of several dense colored point clouds of outdoor environments built by a mobile LiDAR and camera system. The data are composed of two sets with synthetic data from the open source CARLA simulator (700 million points) and real data acquired in the city of Paris (60 million points), hence the name Paris-CARLA-3D. One of the advantages of this dataset is to have simulated the same LiDAR and camera platform in the open source CARLA simulator as the one used to produce the real data. In addition, manual annotation of the classes using the semantic tags of CARLA was performed on the real data, allowing the testing of transfer methods from the synthetic to the real data. The objective of this dataset is to provide a challenging dataset to evaluate and improve methods on difficult vision tasks for the 3D mapping of outdoor environments: semantic segmentation, instance segmentation, and scene completion. For each task, we describe the evaluation protocol as well as the experiments carried out to establish a baseline.