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

Abstract

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.

24 pages

References in corpus (6)

• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
• Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene
• SynthCity: A large scale synthetic point cloud
• KITTI-CARLA: a KITTI-like dataset generated by CARLA Simulator
• Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints
• Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation

Cited by in corpus (4)

• Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation
• Deep Learning on 3D Semantic Segmentation: A Detailed Review
• TUM-FAÇADE: Reviewing and enriching point cloud benchmarks for façade segmentation
• IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments