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
FRUC is a feedforward 3D Gaussian Splatting framework that reconstructs dynamic scenes from uncalibrated collaborative driving views. It uses a visual‑grounded geometric Transformer backbone for one‑shot, calibration‑free inference and introduces an ego‑centric causal occlusion field to model occlusion evolution across agents. The method performs cross‑agent integration as a deterministic residual denoising process, achieving state‑of‑the‑art rendering quality and efficiency on V2X‑Real and UrbanIng‑V2X datasets.
By Yihang Tao, Yu Guo, Zhengru Fang, Haonan An, Yuguang Fang
arXiv:2606. 31834v1 Announce Type: cross Abstract: Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet existing source-free object detection (SFOD) methods rely on heavyweight architectures that prioritize accuracy alone.
By Sairam VCR, Varun Gopal, Poornima Jain, Vineeth N Balasubramanian, Muhammad Haris Khan
VeriFuse is a bounded arbitration framework that integrates vision‑language models (VLMs) into vehicle‑infrastructure cooperative 3D perception. Each agent first generates independent detections, then VeriFuse creates a unified candidate pool of geometric proposals and cross‑source hypotheses. A frozen VLM selects among three actions—SELECT, REFINE, or REJECT—to resolve ambiguity and produce final 3D detections, achieving strong AP50/AP70 scores on the DAIR‑V2X dataset while keeping vehicle‑side BEV AP50 drop minimal under delay.
By Hongyi Lin, Yiyao Liu, Qi Kang, Heye Huang, Yang Liu, Haris Koutsopoulos, Jinhua Zhao
FedCKA introduces a Centered Kernel Alignment (CKA)-based method for federated 3D perception that dynamically balances personalization and globalization. By computing layer-wise feature similarities between local client models and a global consensus model, FedCKA generates client‑specific aggregation masks to selectively share representation‑consistent layers. Experiments on a unified multi‑domain nuScenes benchmark demonstrate that FedCKA surpasses established federated baselines, improving average NDS by 7 percentage points.
By Jolle Verhoog, Ali Burak \"Unal, Holger Caesar
The paper proposes a label‑free 3D perception framework where roadside units (RSUs) act as unsupervised teachers for self‑driving cars. RSUs learn local 3D detectors from unlabeled data and broadcast predictions to passing vehicles, which use these as pseudo‑labels to train an ego‑centric detector. In a CARLA simulation, the method achieves 82.3% AP for vehicle detection, approaching a fully supervised upper bound of 94.4%, and demonstrates scalability and complementarity with existing ego‑centric approaches.
By Zhen Xu, Jinsu Yoo, Cristian Bautista, Zanming Huang, Tai-Yu Pan, Zhenzhen Liu, Katie Z Luo, Mark Campbell, Bharath Hariharan, Wei-Lun Chao