RoScenes: A Large-scale Multi-view 3D Dataset for Roadside Perception
CoRR(2024)
摘要
We introduce RoScenes, the largest multi-view roadside perception dataset,
which aims to shed light on the development of vision-centric Bird's Eye View
(BEV) approaches for more challenging traffic scenes. The highlights of
RoScenes include significantly large perception area, full scene coverage and
crowded traffic. More specifically, our dataset achieves surprising 21.13M 3D
annotations within 64,000 m^2. To relieve the expensive costs of roadside 3D
labeling, we present a novel BEV-to-3D joint annotation pipeline to efficiently
collect such a large volume of data. After that, we organize a comprehensive
study for current BEV methods on RoScenes in terms of effectiveness and
efficiency. Tested methods suffer from the vast perception area and variation
of sensor layout across scenes, resulting in performance levels falling below
expectations. To this end, we propose RoBEV that incorporates feature-guided
position embedding for effective 2D-3D feature assignment. With its help, our
method outperforms state-of-the-art by a large margin without extra
computational overhead on validation set. Our dataset and devkit will be made
available at .
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