AI RESEARCH
Revisiting Token Compression for Accelerating ViT-based Sparse Multi-View 3D Object Detectors
arXiv CS.CV
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ArXi:2604.14563v1 Announce Type: new Vision Transformer (ViT)-based sparse multi-view 3D object detectors have achieved remarkable accuracy but still suffer from high inference latency due to heavy token processing. To accelerate these models, token compression has been widely explored. However, our revisit of existing strategies, such as token pruning, merging, and patch size enlargement, reveals that they often discard informative background cues, disrupt contextual consistency, and lose fine-grained semantics, negatively affecting 3D detection.