Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration

• URL: http://arxiv.org/abs/2502.14156v1
• Date: Wed, 19 Feb 2025 23:53:00 GMT
• Title: Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration
• Authors: Katie Z Luo, Minh-Quan Dao, Zhenzhen Liu, Mark Campbell, Wei-Lun Chao, Kilian Q. Weinberger, Ezio Malis, Vincent Fremont, Bharath Hariharan, Mao Shan, Stewart Worrall, Julie Stephany Berrio Perez,
• Abstract summary: Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. To address these gaps, we present Mixed Signals, a comprehensive V2X dataset featuring 45.1k point clouds and 240.6k bounding boxes. Our dataset provides precisely aligned point clouds and bounding box annotations across 10 classes, ensuring reliable data for perception training.
• Score: 56.75198775820637
• License:
• Abstract: Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X datasets are limited in scope, diversity, and quality. To address these gaps, we present Mixed Signals, a comprehensive V2X dataset featuring 45.1k point clouds and 240.6k bounding boxes collected from three connected autonomous vehicles (CAVs) equipped with two different types of LiDAR sensors, plus a roadside unit with dual LiDARs. Our dataset provides precisely aligned point clouds and bounding box annotations across 10 classes, ensuring reliable data for perception training. We provide detailed statistical analysis on the quality of our dataset and extensively benchmark existing V2X methods on it. Mixed Signals V2X Dataset is one of the highest quality, large-scale datasets publicly available for V2X perception research. Details on the website https://mixedsignalsdataset.cs.cornell.edu/.

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