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20242026
most citedLearning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2026

DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation

Ziyu Shan, Yuheng Zhou, Gaoyuan Wu +3

Mobile manipulation is a fundamental capability that enables robots to interact in expansive environments such as homes and factories. Most existing approaches follow a two-stage p…

cs.RO2026

RoCo Challenge at AAAI 2026: Benchmarking Robotic Collaborative Manipulation for Assembly Towards Industrial Automation

Haichao Liu, Yuheng Zhou, Zhenyu Wu +14

Embodied Artificial Intelligence (EAI) is rapidly developing, gradually subverting previous autonomous systems' paradigms from isolated perception to integrated, continuous action.…

cs.CV2025

Point Cloud Compression and Objective Quality Assessment: A Survey

Yiling Xu, Yujie Zhang, Shuting Xia +6

The rapid growth of 3D point cloud data, driven by applications in autonomous driving, robotics, and immersive environments, has led to criticals demand for efficient compression a…

cs.CV2025

CLIP-PCQA: Exploring Subjective-Aligned Vision-Language Modeling for Point Cloud Quality Assessment

Yating Liu, Yujie Zhang, Ziyu Shan +1

In recent years, No-Reference Point Cloud Quality Assessment (NR-PCQA) research has achieved significant progress. However, existing methods mostly seek a direct mapping function f…

cs.CV20242 cited

Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization

Ziyu Shan, Yujie Zhang, Yipeng Liu +1

No-Reference Point Cloud Quality Assessment (NR-PCQA) aims to objectively assess the human perceptual quality of point clouds without relying on pristine-quality point clouds for r…