2 citations · 2 across the 7 of their papers we have counts for
10 papers
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…
Unrolling Lanczos for Ideal Low-pass Graph Filter Approximation
Parham Eftekhar, Gene Cheung, Mingxiao Liu +3
Low-pass (LP) filtering is a fundamental operation in graph signal processing (GSP). Among finite-order nodal-domain methods, Lanczos-based filtering provides more accurate approxi…
Transforming harmonic coefficients for 3D splat compression
Tam Thuc Do, Philip A. Chou, Gene Cheung
We address the problem of color attribute compression for 3D splats. We show that all images generated by 3D splats are linear in the coefficients for each color channel, each sphe…
Lightweight Transformer for EEG Classification via Balanced Signed Graph Algorithm Unrolling
Junyi Yao, Parham Eftekhar, Gene Cheung +3
Samples of brain signals collected by EEG sensors have inherent anti-correlations that are well modeled by negative edges in a finite graph. To differentiate epilepsy patients from…
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…
Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast
Ji Qi, Tam Thuc Do, Mingxiao Liu +4
Unlike conventional "black-box" transformers with classical self-attention mechanism, we build a lightweight and interpretable transformer-like neural net by unrolling a mixed-grap…
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…
Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors
Tam Thuc Do, Parham Eftekhar, Seyed Alireza Hosseini +2
We build interpretable and lightweight transformer-like neural networks by unrolling iterative optimization algorithms that minimize graph smoothness priors -- the quadratic graph…
Volumetric 3D Point Cloud Attribute Compression: Learned polynomial bilateral filter for prediction
Tam Thuc Do, Philip A. Chou, Gene Cheung
We extend a previous study on 3D point cloud attribute compression scheme that uses a volumetric approach: given a target volumetric attribute function $f : \mathbb{R}^3 \mapsto \m…
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…