V2X-PC: Vehicle-to-everything Collaborative Perception via Point Cluster
Si Liu Zihan Ding Jiahui Fu Hongyu Li Siheng Chen Shifeng Zhang Xu Zhou
The objective of the collaborative vehicle-to-everything perception task is
to enhance the individual vehicle's perception capability through message
communication among neighboring traffic agents. Previous methods focus on
achieving optimal performance within bandwidth limitations and typically adopt
BEV maps as the basic collaborative message units. However, we demonstrate that
collaboration with dense representations is plagued by object feature
destruction during message packing, inefficient message aggregation for
long-range collaboration, and implicit structure representation communication.
To tackle these issues, we introduce a brand new message unit, namely point
cluster, designed to represent the scene sparsely with a combination of
low-level structure information and high-level semantic information. The point
cluster inherently preserves object information while packing messages, with
weak relevance to the collaboration range, and supports explicit structure
modeling. Building upon this representation, we propose a novel framework
V2X-PC for collaborative perception. This framework includes a Point Cluster
Packing (PCP) module to keep object feature and manage bandwidth through the
manipulation of cluster point numbers. As for effective message aggregation, we
propose a Point Cluster Aggregation (PCA) module to match and merge point
clusters associated with the same object. To further handle time latency and
pose errors encountered in real-world scenarios, we propose parameter-free
solutions that can adapt to different noisy levels without finetuning.
Experiments on two widely recognized collaborative perception benchmarks
showcase the superior performance of our method compared to the previous
state-of-the-art approaches relying on BEV maps.