3 citations · 7 across the 11 of their papers we have counts for
11 papers · 1 filter
DPCD: A Quality Assessment Database for Dynamic Point Clouds
Yating Liu, Yujie Zhang, Qi Yang +3
Recently, the advancements in Virtual/Augmented Reality (VR/AR) have driven the demand for Dynamic Point Clouds (DPC). Unlike static point clouds, DPCs are capable of capturing tem…
From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training
Yipeng Liu, Qi Yang, Yujie Zhang +3
We present a novel quality assessment method which can predict the perceptual quality of point clouds from new scenes without available annotations by leveraging the rich prior kno…
Asynchronous Feedback Network for Perceptual Point Cloud Quality Assessment
Yujie Zhang, Qi Yang, Ziyu Shan +1
Recent years have witnessed the success of the deep learning-based technique in research of no-reference point cloud quality assessment (NR-PCQA). For a more accurate quality predi…
Perception-Guided Quality Metric of 3D Point Clouds Using Hybrid Strategy
Yujie Zhang, Qi Yang, Yiling Xu +1
Full-reference point cloud quality assessment (FR-PCQA) aims to infer the quality of distorted point clouds with available references. Most of the existing FR-PCQA metrics ignore t…
Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment
Ziyu Shan, Yujie Zhang, Qi Yang +5
No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference, which have achiev…
PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment
Ziyu Shan, Yujie Zhang, Qi Yang +3
No-reference point cloud quality assessment (NR-PCQA) aims to automatically predict the perceptual quality of point clouds without reference, which has achieved remarkable performa…