On Visual Similarity Based 3D Model Retrieval

TLDR

A large number of 3D models are available on the Web, making content‑based retrieval increasingly necessary. The authors propose a visual similarity‑based 3D model retrieval system that assumes visually similar models will appear similar from all viewing angles. The system encodes one hundred orthogonal projections of each model using Zernike moments and Fourier descriptors, hosts over 10,000 publicly available models on a web platform, and offers a user‑friendly interface for retrieval by drawing 2D shapes. The approach is robust to similarity transformations, noise, and model degeneracy, achieving 42 %, 94 %, and 25 % higher precision‑recall than spherical harmonics, MPEG‑7 Shape 3D, and MPEG‑7 Multiple View descriptors, and it retrieves models in about 2 s for 3D queries and 0.1 s for hand‑drawn 2D shapes. Categories and Subject Descriptors (according to ACM CCS): H.3.1 [Information Storage and Retrieval]: Indexing Methods.

Abstract

Abstract A large number of 3D models are created and available on the Web, since more and more 3D modelling anddigitizing tools are developed for ever increasing applications. The techniques for content‐based 3D model retrievalthen become necessary. In this paper, a visual similarity‐based 3D model retrieval system is proposed.This approach measures the similarity among 3D models by visual similarity, and the main idea is that if two 3Dmodels are similar, they also look similar from all viewing angles. Therefore, one hundred orthogonal projectionsof an object, excluding symmetry, are encoded both by Zernike moments and Fourier descriptors as features forlater retrieval. The visual similarity‐based approach is robust against similarity transformation, noise, model degeneracyetc., and provides 42%, 94% and 25% better performance (precision‐recall evaluation diagram) thanthree other competing approaches: (1) the spherical harmonics approach developed by Funkhouser et al., (2) theMPEG‐7 Shape 3D descriptors, and (3) the MPEG‐7 Multiple View Descriptor. The proposed system is on the Webfor practical trial use ( http://3d.csie.ntu.edu.tw ), and the database contains more than 10,000 publicly available3D models collected from WWW pages. Furthermore, a user friendly interface is provided to retrieve 3D modelsby drawing 2D shapes. The retrieval is fast enough on a server with Pentium IV 2.4 GHz CPU, and it takes about2 seconds and 0.1 seconds for querying directly by a 3D model and by hand drawn 2D shapes, respectively. Categories and Subject Descriptors (according to ACM CCS): H.3.1 [Information Storage and Retrieval]: Indexing Methods