mlsanityData to 14 Oct 2026

Is Discretization Fusion All You Need for Collaborative Perception?

arXiv 2503.13946 ↗First posted 18 Mar 2025cs.CVcited by 6

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Abstract (arXiv)

Collaborative perception in multi-agent system enhances overall perceptual capabilities by facilitating the exchange of complementary information among agents. Current mainstream collaborative perception methods rely on discretized feature maps to conduct fusion, which however, lacks flexibility in extracting and transmitting the informative features and can hardly focus on the informative features during fusion. To address these problems, this paper proposes a novel Anchor-Centric paradigm for Collaborative Object detection (ACCO). It avoids grid precision issues and allows more flexible and efficient anchor-centric communication and fusion. ACCO is composed by three main components: (1) Anchor featuring block (AFB) that targets to generate anchor proposals and projects prepared anchor queries to image features. (2) Anchor confidence generator (ACG) is designed to minimize communication by selecting only the features in the confident anchors to transmit. (3) A local-global fusion module, in which local fusion is anchor alignment-based fusion (LAAF) and global fusion is conducted by spatial-aware cross-attention (SACA). LAAF and SACA run in multi-layers, so agents conduct anchor-centric fusion iteratively to adjust the anchor proposals. Comprehensive experiments are conducted to evaluate ACCO on OPV2V and Dair-V2X datasets, which demonstrate ACCO's superiority in reducing the communication volume, and in improving the perception range and detection performances. Code can be found at: hrefhttps://github.com/sidiangongyuan/ACCOhttps://github.com/sidiangongyuan/ACCO.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 35 of its 38 references that are in the map. First posted on arXiv 18 Mar 2025.

Builds on

35 of 38 references.

References · 35

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23 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2025INSTINCT: Instance-Level Interaction Architecture for Query-Based Collaborative Perceptioncited by 5
  2. 2026EIMC: Efficient Instance-aware Multi-modal Collaborative Perceptioncited by 4
  3. 2026BOLT: Online Lightweight Adaptation for Preparation-Free Heterogeneous Cooperative Perceptioncited by 1
  4. 2026UECP: Uncertainty-Enhanced Collaborative Perceptioncited by 0
  5. 2026CERF: Communication-Efficient and Retraining-Free Collaborative Perceptioncited by 0
  6. 2026CoVLM-Bench: A Real-World Benchmark for Cooperative Driving Question Answering and Planningcited by 0