most citedEnhancing context models for point cloud geometry compression with context feature residuals and multi-loss

19 citations · 70 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV202415 cited

Rate-Distortion Optimized Skip Coding of Region Adaptive Hierarchical Transform Coefficients for MPEG G-PCC

Zehan Wang, Yuxuan Wei, Hui Yuan +2

Three-dimensional (3D) point clouds are becoming more and more popular for representing 3D objects and scenes. Due to limited network bandwidth, efficient compression of 3D point c…

eess.IV20243 cited

Cuboid-Net: A Multi-Branch Convolutional Neural Network for Joint Space-Time Video Super Resolution

Congrui Fu, Hui Yuan, Hongji Xu +2

The demand for high-resolution videos has been consistently rising across various domains, propelled by continuous advancements in science, technology, and societal. Nonetheless, c…

eess.IV2024

PCAC-GAN: A Sparse-Tensor-Based Generative Adversarial Network for 3D Point Cloud Attribute Compression

Xiaolong Mao, Hui Yuan, Xin Lu +2

Learning-based methods have proven successful in compressing geometric information for point clouds. For attribute compression, however, they still lag behind non-learning-based me…

eess.IV202415 cited

OMR-NET: a two-stage octave multi-scale residual network for screen content image compression

Shiqi Jiang, Ting Ren, Congrui Fu +2

Screen content (SC) differs from natural scene (NS) with unique characteristics such as noise-free, repetitive patterns, and high contrast. Aiming at addressing the inadequacies of…

eess.IV202417 cited

Enhancing octree-based context models for point cloud geometry compression with attention-based child node number prediction

Chang Sun, Hui Yuan, Xiaolong Mao +2

In point cloud geometry compression, most octreebased context models use the cross-entropy between the onehot encoding of node occupancy and the probability distribution predicted…

eess.IV202419 cited

Enhancing context models for point cloud geometry compression with context feature residuals and multi-loss

Chang Sun, Hui Yuan, Shuai Li +2

In point cloud geometry compression, context models usually use the one-hot encoding of node occupancy as the label, and the cross-entropy between the one-hot encoding and the prob…