2 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
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…
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…