Sparse and noisy millimeter-wave radar point cloud observations often correspond to multiple plausible human poses, making deterministic pose estimation fundamentally ill-posed. Yet existing radar methods remain deterministic, collapsing this ambiguity into a single estimate.
arXiv:2609.34220v2 Announce Type: replace-cross
Abstract: Assistive robots increasingly operate in many human-centered environments and perform various human-robot interaction (HRI) tasks, such as ob...
By Junqiao Fan, Yuxuan Hu, Bofan Lyu, Yanshuo Lu, Pengfei Liu, Jiarui Zhang, Fangqiang Ding, Lihua Xie, Gen Li, Jianfei Yang
arXiv:2609.22857v1 Announce Type: new
Abstract: Dynamic object segmentation and ego-motion estimation are closely coupled problems in autonomous driving, as accurate ego-motion estimation typically r...
By Astik Srivastava, Suhani Grover, Avinash Sharma, Madhava Krishna
Assistive robots increasingly operate in many human-centered environments and perform various human-robot interaction (HRI) tasks, such as object delivery. However, most existing HRI systems rely on R...
The review surveys 4D millimeter‑wave radar perception algorithms for autonomous driving, covering signal processing, object detection, semantic segmentation, motion estimation, occupancy prediction, and dynamic scene reconstruction. It organizes the field by perception tasks, discusses radar fundamentals, data representations, and quality‑enhancement methods, and compares radar‑only learning, multimodal fusion, and cross‑modal supervision. The paper also summarizes datasets, annotations, evaluation protocols, and outlines common challenges and future research directions.
By Xumin Wu, Jun Zhou, Jilin Mei, Chen Min, Yu Hu
arXiv:2607. 09629v1 Announce Type: cross Abstract: Reliable autonomous driving requires full-scene perception that couples foreground objects with dense semantic layout.
By Xiaokai Bai, Lianqing Zheng, Runwei Guan, Songkai Wang, Siyuan Cao, Hui-liang Shen