Hugging Face Trending Papers

Continuous Ground-Truth Construction and a Recovery Policy for Air--Water Robotic Tracking

arXiv AI
5d ago

Continuous Ground-Truth Construction and a Recovery Policy for Air--Water Robotic Tracking

The paper presents a pipeline for constructing accurate ground‑truth annotations for visual tracking across the air–water interface, synchronizing camera frames with motion‑capture data and correcting underwater projections. It introduces a Cross‑Medium Recovery Policy (CMRP) that uses confidence‑triggered template selection to improve MixFormerV2’s tracking performance without retraining the backbone. CMRP raises Macro Success AUC by 2.95 points and increases tracking coverage while reducing recovery latency.

By Jiangong Xiao, Zhe Sun, Kanzhong Yao, Yuanbo Bi, Haofei Zhao, Ruixuan Hu, Guan Huang, Xuelong Li
arXiv AI
Sep 23

RiverVLN: Phase-Grounded Temporal Vision--Language Navigation for Unmanned Surface Vehicles

RiverVLN introduces the first benchmark for long‑horizon vision‑language navigation (VLN) of unmanned surface vehicles (USVs) in continuous riverine motion. The PGT‑NAV framework converts navigation instructions into an ordered sequence of visually verifiable semantic phases, maintaining an active phase online through grounded visual and motion evidence. This phase‑grounded approach reduces recursive position and heading drift, achieving a 0.79 success rate in Unity‑ROS closed‑loop tests and demonstrating transfer to real‑world USV deployment.

By Jieling Wu, Yuehao Huang, Jiajun Lv, Tao Huang, Yong Liu, Weiwei Liu
arXiv Computer Vision
Sep 7

AquaBEV: Monocular Underwater BEV Occupancy with 3D Sonar Supervision

AquaBEV is a monocular underwater occupancy model that predicts local bird’s‑eye‑view (BEV) occupancy from a single RGB image. It uses paired 3D imaging sonar data as geometric supervision during training, mapping visual features into a calibration‑free polar representation and decoding along the range dimension before reconstructing Cartesian BEV coordinates. In a controlled underwater occupancy benchmark, AquaBEV outperforms the strongest transferred baseline with 31.4 % Visible IoU and 38.6 % Observed IoU, achieving 4.0 % and 4.3 % relative improvements respectively.

By Trung Tien Dong, Shengji Jin, Chen Chen, Yi Sheng, Xiaomin Lin
arXiv AI
Sep 25

Underwater C3-JEPA: An Object-Centric Cross-View World Model for ROV Salvage

Underwater C³-JEPA is an object‑centric, cross‑view predictive world model designed for near‑field heavy‑load ROV salvage. It encodes synchronized multi‑camera RGB observations and vehicle control signals into task‑object and context tokens, fuses cross‑camera evidence via held‑out‑view attention, and predicts future states conditioned on control without using contact sensors. The model demonstrates superior transfer of task‑relevant information to downstream probes compared to a reconstruction‑free baseline, supports model‑predictive control and imagined‑rollout training, and validates its effectiveness on real underwater video by accurately recovering withheld camera states and maintaining predictive lead over persistence.

By Yuncong Yang, Jinlong Li, Yulong Xue, Feng Wu, Chunwen Zhang, Lei Qiao, Xuyang Wang