  This paper presents CORE, a conceptually simple, effective and
communication-efficient model for multi-agent cooperative perception. It
addresses the task from a novel perspective of cooperative reconstruction,
based on two key insights: 1) cooperating agents together provide a more
holistic observation of the environment, and 2) the holistic observation can
serve as valuable supervision to explicitly guide the model learning how to
reconstruct the ideal observation based on collaboration. CORE instantiates the
idea with three major components: a compressor for each agent to create more
compact feature representation for efficient broadcasting, a lightweight
attentive collaboration component for cross-agent message aggregation, and a
reconstruction module to reconstruct the observation based on aggregated
feature representations. This learning-to-reconstruct idea is task-agnostic,
and offers clear and reasonable supervision to inspire more effective
collaboration, eventually promoting perception tasks. We validate CORE on
OPV2V, a large-scale multi-agent percetion dataset, in two tasks, i.e., 3D
object detection and semantic segmentation. Results demonstrate that the model
achieves state-of-the-art performance on both tasks, and is more
communication-efficient.
