Mask4Former: Mask Transformer for 4D Panoptic Segmentation
arxiv(2023)
摘要
Accurately perceiving and tracking instances over time is essential for the
decision-making processes of autonomous agents interacting safely in dynamic
environments. With this intention, we propose Mask4Former for the challenging
task of 4D panoptic segmentation of LiDAR point clouds. Mask4Former is the
first transformer-based approach unifying semantic instance segmentation and
tracking of sparse and irregular sequences of 3D point clouds into a single
joint model. Our model directly predicts semantic instances and their temporal
associations without relying on hand-crafted non-learned association strategies
such as probabilistic clustering or voting-based center prediction. Instead,
Mask4Former introduces spatio-temporal instance queries that encode the
semantic and geometric properties of each semantic tracklet in the sequence. In
an in-depth study, we find that promoting spatially compact instance
predictions is critical as spatio-temporal instance queries tend to merge
multiple semantically similar instances, even if they are spatially distant. To
this end, we regress 6-DOF bounding box parameters from spatio-temporal
instance queries, which are used as an auxiliary task to foster spatially
compact predictions. Mask4Former achieves a new state-of-the-art on the
SemanticKITTI test set with a score of 68.4 LSTQ.
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