Title: Perception Through Sparsity: Fusing and Enhancing Multi-Agent Sparse Representation with Deformable Cross-Attention
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Autonomous Driving; Perception; Multi-Agent Perceptioin;

Abstract: Multi‑agent perception has gained significant attention for its ability to share information among connected automated vehicles (CAVs) and smart infrastructure, thus mitigating occlusions and extending effective sensing range.
Despite this progress, research on radar-based cooperative perception has been 
constrained by limited datasets, where existing benchmarks either provide only 
partial radar views or a small number of frames, making it difficult to fully 
study radar’s potential in V2X perception. To address this gap, we introduce V2XSet-R, the first large-scale dataset 
that provides complete 360 degree radar coverage from both vehicles 
and infrastructure, with 150k radar frames and 170k annotated 3D bounding 
boxes. This dataset significantly expands the scale and diversity of radar data, 
enabling systematic study of radar-based cooperative perception and fusion. Building on this resource, we propose SparseFusion, a dual-stage fusion 
framework tailored to sparse multi-agent perception. Unlike prior position-wise 
self-attention designs that compute affinity scores only among voxels at the 
same BEV location, SparseFusion aggregates cross-voxel context via a 
query-based deformable attention module that adaptively samples informative 
regions across space and agents. This design overcomes sparsity-induced 
degeneration and enhances feature interaction across agents
, and effectively generalizes to camera BEV features. These results demonstrate that SparseFusion is a precise, efficient, and modality‑agnostic fusion method for cooperative perception.