no code implementations • 22 Nov 2023 • 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 $f : \mathbb{R}^3 \mapsto \mathbb{R}$, we quantize and encode parameters $\theta$ that characterize $f$ at the encoder, for reconstruction $f_{\hat{\theta}}(\mathbf(x))$ at known 3D points $\mathbf(x)$ at the decoder.
no code implementations • 22 Nov 2023 • 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 $\theta$ of a continuous attribute function $f: \mathbb{R}^3 \mapsto \mathbb{R}$ are quantized to $\hat{\theta}$ and encoded, so that discrete samples $f_{\hat{\theta}}(\mathbf{x}_i)$ can be recovered at known 3D points $\mathbf{x}_i \in \mathbb{R}^3$ at the decoder.
no code implementations • 1 Apr 2023 • Tam Thuc Do, Philip A. Chou, Gene Cheung
We study 3D point cloud attribute compression using a volumetric approach: given a target volumetric attribute function $f : \mathbb{R}^3 \rightarrow \mathbb{R}$, we quantize and encode parameter vector $\theta$ that characterizes $f$ at the encoder, for reconstruction $f_{\hat{\theta}}(\mathbf{x})$ at known 3D points $\mathbf{x}$'s at the decoder.
no code implementations • 2 Mar 2022 • Saghar Bagheri, Tam Thuc Do, Gene Cheung, Antonio Ortega
Transform coding to sparsify signal representations remains crucial in an image compression pipeline.