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Shanshe Wang
Researcher at Peking University
Publications - 180
Citations - 2388
Shanshe Wang is an academic researcher from Peking University. The author has contributed to research in topics: Computer science & Coding (social sciences). The author has an hindex of 18, co-authored 136 publications receiving 1188 citations. Previous affiliations of Shanshe Wang include City University of Hong Kong & Harbin Institute of Technology.
Papers
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Proceedings ArticleDOI
Improving Robustness and Accuracy via Relative Information Encoding in 3D Human Pose Estimation
TL;DR: Zhang et al. as discussed by the authors proposed a relative information encoding method that yields positional and temporal enhanced representations to resist the interference of global motion on the prediction results, which outperforms state-of-the-art methods on two public datasets.
Proceedings ArticleDOI
Cluster-Based Point Cloud Coding with Normal Weighted Graph Fourier Transform
TL;DR: A novel point cloud compression method for attributes, based on geometric clustering and Normal Weighted Graph Fourier Transform (NWGFT) is proposed, which enables efficient representation with less cost.
Proceedings ArticleDOI
A study on the rate distortion modeling for High Efficiency Video Coding
Siwei Ma,Junjun Si,Shanshe Wang +2 more
TL;DR: A separate model for header bits and coefficient bits respectively are proposed for rate modeling and the quantization distortion and the distortion reduction induced by SAO/ALF are modeled jointly, showing that the coding rate and distortion can be estimated accurately.
Journal ArticleDOI
GPU-Based Hierarchical Motion Estimation for High Efficiency Video Coding
TL;DR: A GPU-based low delay parallel ME scheme for high efficiency video coding (HEVC) is proposed by optimizing the ME process in a coding tree unit (CTU), prediction unit (PU), and motion vector (MV) layers, which can completely save the encoding time for ME on CPU.
Posted Content
Predictive Generalized Graph Fourier Transform for Attribute Compression of Dynamic Point Clouds.
TL;DR: This work proposes a complete compression framework for attributes of 3D dynamic point clouds, focusing on optimal inter-coding and refined motion estimation via efficient registration prior to inter-prediction, which searches the temporal correspondence between adjacent frames of irregular point clouds.