Scalable Scene Flow From Point Clouds in the Real World

نویسندگان

چکیده

Autonomous vehicles operate in highly dynamic environments necessitating an accurate assessment of which aspects a scene are moving and where they to. A popular approach to 3D motion estimation, termed flow, is employ point cloud data from consecutive LiDAR scans, although such approaches have been limited by the small size real-world, annotated data. In this work, we introduce new large-scale dataset for flow estimation derived corresponding tracked objects, $\sim 1,000\times$ larger than previous real-world datasets terms number frames. We demonstrate how works were bounded amount real available, suggesting that required achieve state-of-the-art predictive performance. Furthermore, show heuristics as down-sampling heavily degrade performance, motivating class models tractable on full cloud. To address issue, FastFlow3D architecture provides time inference Additionally, design human-interpretable metrics better capture world accounting ego-motion providing breakdowns per object type. hope may provide opportunities developing systems.

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ژورنال

عنوان ژورنال: IEEE robotics and automation letters

سال: 2022

ISSN: ['2377-3766']

DOI: https://doi.org/10.1109/lra.2021.3139542