4 citations · 5 across the 16 of their papers we have counts for
12 papers · 1 filter
OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback
Kai Luo, Hao Shi, Kunyu Peng +4
To address panoramic distortion, large search space, and identity ambiguity under a 360° FoV, OmniTrack++ adopts a feedback-driven framework that progressively refines perception w…
DepTR-MOT: Unveiling the Potential of Depth-Informed Trajectory Refinement for Multi-Object Tracking
Buyin Deng, Lingxin Huang, Kai Luo +2
Visual Multi-Object Tracking (MOT) is a crucial component of robotic perception, yet existing Tracking-By-Detection (TBD) methods often rely on 2D cues, such as bounding boxes and…
Visually Grounded Narratives: Reducing Cognitive Burden in Researcher-Participant Interaction
Runtong Wu, Jiayao Song, Fei Teng +3
Narrative inquiry has been one of the prominent application domains for the analysis of human experience, aiming to know more about the complexity of human society. However, resear…
QuaDreamer: Controllable Panoramic Video Generation for Quadruped Robots
Sheng Wu, Fei Teng, Hao Shi +4
Panoramic cameras, capturing comprehensive 360-degree environmental data, are suitable for quadruped robots in surrounding perception and interaction with complex environments. How…
Hallucinating 360°: Panoramic Street-View Generation via Local Scenes Diffusion and Probabilistic Prompting
Fei Teng, Kai Luo, Sheng Wu +6
Panoramic perception holds significant potential for autonomous driving, enabling vehicles to acquire a comprehensive 360° surround view in a single shot. However, autonomous drivi…
NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models
Siyu Li, Fei Teng, Yihong Cao +3
Birds' Eye View (BEV) semantic segmentation is an indispensable perception task in end-to-end autonomous driving systems. Unsupervised and semi-supervised learning for BEV tasks, a…
Out-of-Distribution Semantic Occupancy Prediction
Yuheng Zhang, Mengfei Duan, Kunyu Peng +6
3D semantic occupancy prediction is crucial for autonomous driving, providing a dense, semantically rich environmental representation. However, existing methods focus on in-distrib…
Go Beyond Earth: Understanding Human Actions and Scenes in Microgravity Environments
Di Wen, Lei Qi, Kunyu Peng +9
Despite substantial progress in video understanding, most existing datasets are limited to Earth's gravitational conditions. However, microgravity alters human motion, interactions…
Panoramic Out-of-Distribution Segmentation
Mengfei Duan, Yuheng Zhang, Yihong Cao +5
Panoramic imaging enables capturing 360° images with an ultra-wide Field-of-View (FoV) for dense omnidirectional perception, which is critical to applications such as autonomous dr…
Omnidirectional Multi-Object Tracking
Kai Luo, Hao Shi, Sheng Wu +6
Panoramic imagery, with its 360° field of view, offers comprehensive information to support Multi-Object Tracking (MOT) in capturing spatial and temporal relationships of surroundi…
Unveiling the Potential of Segment Anything Model 2 for RGB-Thermal Semantic Segmentation with Language Guidance
Jiayi Zhao, Fei Teng, Kai Luo +4
The perception capability of robotic systems relies on the richness of the dataset. Although Segment Anything Model 2 (SAM2), trained on large datasets, demonstrates strong percept…
LFX: Towards Unified Light Field Dense Semantic Segmentation and Salient Object Detection
Fei Teng, Lingxin Huang, Buyin Deng +8
Light field cameras capture multi-view observations within a single exposure. However, existing studies are typically tailored to specific LF representations, leaving the field wit…