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.

协作车辆到所有感知任务通过邻近交通代理之间的消息通信提高各个车辆的感知能力。我们提出了一种全新的消息单元，即点簇，并建立了协作感知的新框架 V2X-PC，以解决稠密表示中的目标特征破坏、长距离协作中的信息聚合效率和隐含结构表示通信的问题。通过在两个被广泛认可的协作感知基准上进行实验，我们展示了我们方法相对于依赖于 BEV 地图的先前最先进方法的优越性能。