arXiv:2607. 08970v1 Announce Type: cross Abstract: Recent benchmarks for VLMs largely assess single- or limited-view perception, leaving untested the core cognitive ability to integrate observations across viewpoints into a coherent, world-centric (allocentric) 3D mental model.
By Hantao Zhang, Jinru Sui, Ed Li, Dirk Bergemann, Zhuoran Yang
arXiv:2607. 19036v1 Announce Type: cross Abstract: V2X collaborative object detection features overcoming the limitations of single-vehicle systems by aggregating environmental features from multiple collaborative agents.
By Zhihao Yang, Zhiyu Xiang, Peng Xu, Tianyu Pu, Kai Wang, Eryun Liu, Dongping Zhang, Yong Ding
arXiv:2609.21597v1 Announce Type: new
Abstract: Monocular 6-DoF pose estimation of non-cooperative targets is important for on-orbit servicing and debris removal. A single-image estimator can confuse...
By Andr\'e Lopo, Atabak Dehban, Rodrigo Ventura
arXiv:2607. 19942v1 Announce Type: cross Abstract: This work introduces G-MAD, an open-source framework that uses Arma3 to generate synchronized multi-view RGB-T data for aerial object detection.
By Yechan Kim, JongHyun Park, Dongho Yoon, Namhoon Jung, Moongu Jeon
GeoMAD is a multi‑view anomaly detection framework that fuses multiple camera viewpoints while maintaining geometric awareness and scalability to multi‑class industrial settings. It introduces a Cross‑view Deformable Fusion Module (CDFM) that learns view‑pair‑specific sampling offsets on 2D feature maps, enabling hierarchical cross‑view correspondence without camera calibration or voxel construction. Additionally, Distributional View Alignment (DVA) provides a self‑supervised loss that aligns bottleneck distributions across views, ensuring global consistency without pixel‑level correspondence. Together, CDFM and DVA achieve geometry‑aware, distribution‑consistent fusion and demonstrate strong detection and localization performance on Real‑IAD and MANTA‑Tiny datasets.
By Shang-Fu Chen, Jhih-Ciang Wu, Kuan-Chuan Peng, Wen-Huang Cheng, Kai-Lung Hua
arXiv:2603. 06576v2 Announce Type: replace-cross Abstract: The integration of Large Language Models (LLMs) into autonomous driving has attracted growing interest for their strong reasoning and semantic understanding abilities, which are essential for handling complex decision-making and long-tail scenarios.
By Thomas Monninger, Shaoyuan Xie, Qi Alfred Chen, Sihao Ding