SynLiDAR: Synthetic Sequential Point Clouds with Point-Wise Annotations

SynLiDAR is a synthetic LiDAR sequential point cloud dataset featuring point-wise semantic annotations, developed by xiaoaoran and published at AAAI 2022. It provides large-scale simulated sensor data specifically designed for 3D scene understanding and domain adaptation research.

Use Cases

• 3D semantic segmentation using point-wise semantic labels
• Synthetic-to-real domain adaptation for LiDAR-based perception
• Sequential point cloud processing to analyze temporal consistency in scans

Strengths

• Point-wise semantic annotations for 3D segmentation
• Sequential data format supports temporal feature learning
• MIT license allows for flexible research and commercial use

Limitations

• Synthetic-to-real domain gap may affect performance on physical sensor data
• Metadata does not specify the number of semantic classes or total frames

Provenance

Source

AAAI 2022 Publication (SynLiDAR)

Collection Method

synthetic

Freshness

Last updated December 2025; based on a 2022 research publication.