View subspaces for indexing and retrieval of 3D models

TL;DR

This paper proposes a novel view-based 3D shape retrieval method using data-driven subspace models like PCA, ICA, and NMF to improve shape description and categorization, tested on the PSB database.

Contribution

It introduces the use of subspace models for view-based 3D object retrieval and demonstrates their effectiveness over classical shape descriptors.

Findings

Data-driven subspace models improve shape retrieval accuracy.

Categorizing shapes by eigenvalue spread enhances retrieval performance.

The proposed methods outperform classical descriptors on the PSB database.

Abstract

View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithms