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Self-Supervised Pillar Motion Learning for Autonomous Driving
[article]
2021
arXiv
pre-print
Autonomous driving can benefit from motion behavior comprehension when interacting with diverse traffic participants in highly dynamic environments. Recently, there has been a growing interest in estimating class-agnostic motion directly from point clouds. Current motion estimation methods usually require vast amount of annotated training data from self-driving scenes. However, manually labeling point clouds is notoriously difficult, error-prone and time-consuming. In this paper, we seek to
arXiv:2104.08683v1
fatcat:g7xx35yltzafrobimjwnwxvcvm