← Back to all news
arXiv Computer Vision By Melih Yazgan, Timon M\"uller, J. Marius Z\"ollner

Dense Coverage, Sparse Refinement: Byte-Constrained Cooperative Perception

Read the original on arXiv Computer Vision →

The paper introduces a byte‑constrained cooperative perception framework that balances dense coverage with sparse refinement. Each vehicle sends a highly compressed coarse Bird’s‑Eye‑View (BEV) layer covering the entire map and uses the remaining bandwidth to transmit high‑resolution patches selected by a Task‑Aware Benefit Selector. Experiments on DAIR‑V2X and OPV2V demonstrate that this coverage‑refinement strategy achieves superior accuracy‑payload trade‑offs, reaching 0.60 AP@0.7 with only 1.87 KB per non‑ego agent.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Sep 11

HeteroPROMPT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework

HeteroPROMPT is a real‑time, privacy‑preserving framework for heterogeneous collaborative perception in autonomous systems. It aligns features from diverse sensors and models into a unified ego‑centric space using modular prompts and lightweight tuning, while keeping encoders and fusion stacks frozen. The system employs a metadata‑free autoencoder for modality classification and routing, achieving higher average precision on OPV2V‑H and V2XSet datasets with far fewer trainable parameters.

By Armin Maleki, Hayder Radha
agentsbenchmarkssafety
More like this →
arXiv Machine Learning
Sep 24

PEARL: A Lightweight Prompt-based Feature Interpreter Framework for Real-Time, Anonymous, and Heterogeneous Collaborative Perception

PEARL is a lightweight, prompt‑embedding framework designed for real‑time, anonymous, and heterogeneous collaborative perception. It uses two parallel, low‑rank visual prompt interpreters—sparse‑detection (LWSD) and dense, domain‑invariant (LWDDI)—to align features and select the appropriate interpreter for newly joining agents without needing their configurations. Experiments on simulated and real datasets show that PEARL improves average precision by 8.2% over random selection, runs in 1.67 ms, and reduces communication cost by up to 34.7× while outperforming state‑of‑the‑art offline methods by 5.6% AP.

By Armin Maleki, Hayder Radha
ragagentsbenchmarks
More like this →