arXiv Computer Vision
Oct 2

EgoRefine: Ego-Referenced Predictive Alignment and Trajectory-Conditioned Reliability-Aware Fusion for Asynchronous Collaborative Perception

arXiv:2610.00319v1 Announce Type: new Abstract: Collaborative perception enables connected agents to share complementary observations for 3D object detection, extending sensing range and mitigating o...

By Lingzhao Kong, Yongsheng Zang, Yu Kang, Kailun Yang, Jie Fu, Yukun Zuo, Zhiyong Li
Hugging Face Trending Papers
Aug 10

Towards Collaborative Joint Perception and Prediction: Framework, Baseline Evaluation, and Deployment Perspectives

Connected Autonomous Vehicles (CAVs) increasingly exploit Vehicle-to-Everything (V2X) communication to exchange multi-source sensor information, enabling advanced Collaborative Perception (CP) capabilities. Extending beyond these capabilities, this work focuses on Collaborative Joint Perception and Prediction (Co-P&P), a paradigm that unifies CP with motion prediction to mitigate two persistent challenges: the accumulation of perception errors and visual occlusions.

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
arXiv Computer Vision
Sep 24

Bend the Clock: Predicting Ahead to Beat Latency in Event-Based Object Detection

The paper introduces ChronoFuse, a causal availability-time detector that predicts object states at the time its output becomes available rather than at the observation timestamp, addressing the latency mismatch in event-based multi-object detection. ChronoFuse performs lightweight cross-time fusion over a multi-scale feature hierarchy, adding only 0.17 M parameters and 0.84 ms latency overhead. It recovers a large portion of accuracy lost to latency, achieving up to 20.95 sAP on EV‑Flying data compared to 2.25 sAP for the strongest standard detector.

By Biswadeep Sen, Benoit R. Cottereau, Nicolas Cuperlier, Terence Sim