DeepAI AI Chat
Log In Sign Up

Compression of animated 3D models using HO-SVD

10/04/2013
by   Michał Romaszewski, et al.
0

This work presents an analysis of Higher Order Singular Value Decomposition (HO-SVD) applied to lossy compression of 3D mesh animations. We describe strategies for choosing a number of preserved spatial and temporal components after tensor decomposition. Compression error is measured using three metrics (MSE, Hausdorff, MSDM). Results are compared with a method based on Principal Component Analysis (PCA) and presented on a set of animations with typical mesh deformations.

READ FULL TEXT
06/24/2021

Regularisation for PCA- and SVD-type matrix factorisations

Singular Value Decomposition (SVD) and its close relative, Principal Com...
04/13/2018

Regularized Singular Value Decomposition and Application to Recommender System

Singular value decomposition (SVD) is the mathematical basis of principa...
12/07/2021

Enhancing the SVD Compression

Orthonormality is the foundation of matrix decomposition. For example, S...
11/04/2021

Continuous Encryption Functions for Security Over Networks

This paper presents a study of continuous encryption functions (CEFs) of...
06/15/2018

TTHRESH: Tensor Compression for Multidimensional Visual Data

Memory and network bandwidth are decisive bottlenecks when handling high...
06/30/2022

Language model compression with weighted low-rank factorization

Factorizing a large matrix into small matrices is a popular strategy for...