Which2comm: An Efficient Collaborative Perception Framework for 3D Object Detection
Journal:
arXiv
Published Date:
Mar 21, 2025
Abstract
Collaborative perception allows real-time inter-agent information exchange
and thus offers invaluable opportunities to enhance the perception capabilities
of individual agents. However, limited communication bandwidth in practical
scenarios restricts the inter-agent data transmission volume, consequently
resulting in performance declines in collaborative perception systems. This
implies a trade-off between perception performance and communication cost. To
address this issue, we propose Which2comm, a novel multi-agent 3D object
detection framework leveraging object-level sparse features. By integrating
semantic information of objects into 3D object detection boxes, we introduce
semantic detection boxes (SemDBs). Innovatively transmitting these
information-rich object-level sparse features among agents not only
significantly reduces the demanding communication volume, but also improves 3D
object detection performance. Specifically, a fully sparse network is
constructed to extract SemDBs from individual agents; a temporal fusion
approach with a relative temporal encoding mechanism is utilized to obtain the
comprehensive spatiotemporal features. Extensive experiments on the V2XSet and
OPV2V datasets demonstrate that Which2comm consistently outperforms other
state-of-the-art methods on both perception performance and communication cost,
exhibiting better robustness to real-world latency. These results present that
for multi-agent collaborative 3D object detection, transmitting only
object-level sparse features is sufficient to achieve high-precision and robust
performance.
