2 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
TSGL: Teacher-Student Graph Learning for 3DGS Compression
Matin Bani Saedi, Matthew Kyan, Gene Cheung
3D Gaussian Splatting (3DGS) is a popular representation for novel view synthesis. However, 3DGS contains millions of Gaussian primitives, each with rich attributes, resulting in l…
Deep Unrolling of Sparsity-Induced RDO for 3D Point Cloud Attribute Coding
Tam Thuc Do, Philip A. Chou, Gene Cheung
Given encoded 3D point cloud geometry available at the decoder, we study the problem of lossy attribute compression in a multi-resolution B-spline projection framework. A target co…
Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer
Seyed Alireza Hosseini, Tam Thuc Do, Gene Cheung +1
An image denoiser can be used for a wide range of restoration problems via the Plug-and-Play (PnP) architecture. In this paper, we propose a general framework to build an interpret…
Learned Nonlinear Predictor for Critically Sampled 3D Point Cloud Attribute Compression
Tam Thuc Do, Philip A. Chou, Gene Cheung
We study 3D point cloud attribute compression via a volumetric approach: assuming point cloud geometry is known at both encoder and decoder, parameters of a continuous attribut…