Instantaneous Perception of Moving Objects in 3D
arxiv(2024)
摘要
The perception of 3D motion of surrounding traffic participants is crucial
for driving safety. While existing works primarily focus on general large
motions, we contend that the instantaneous detection and quantification of
subtle motions is equally important as they indicate the nuances in driving
behavior that may be safety critical, such as behaviors near a stop sign of
parking positions. We delve into this under-explored task, examining its unique
challenges and developing our solution, accompanied by a carefully designed
benchmark. Specifically, due to the lack of correspondences between consecutive
frames of sparse Lidar point clouds, static objects might appear to be moving -
the so-called swimming effect. This intertwines with the true object motion,
thereby posing ambiguity in accurate estimation, especially for subtle motions.
To address this, we propose to leverage local occupancy completion of object
point clouds to densify the shape cue, and mitigate the impact of swimming
artifacts. The occupancy completion is learned in an end-to-end fashion
together with the detection of moving objects and the estimation of their
motion, instantaneously as soon as objects start to move. Extensive experiments
demonstrate superior performance compared to standard 3D motion estimation
approaches, particularly highlighting our method's specialized treatment of
subtle motions.
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