GIFT: A Real-time and Scalable 3D Shape Search Engine

TL;DR

GIFT is a real-time, scalable 3D shape search engine that combines GPU acceleration and dual inverted files to achieve fast retrieval and high accuracy in shape recognition tasks.

Contribution

This paper introduces GIFT, the first 3D shape search engine that uses GPU-accelerated projection and dual inverted files for real-time, scalable retrieval.

Findings

Retrieves 3D shapes within one second per query.

Outperforms state-of-the-art methods in accuracy on benchmark datasets.

Efficiently combines GPU acceleration with inverted file indexing.

Abstract

Projective analysis is an important solution for 3D shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations from different view points. Although multiple informative and discriminative views are utilized, most projection-based retrieval systems suffer from heavy computational cost, thus cannot satisfy the basic requirement of scalability for search engines. In this paper, we present a real-time 3D shape search engine based on the projective images of 3D shapes. The real-time property of our search engine results from the following aspects: (1) efficient projection and view feature extraction using GPU acceleration; (2) the first inverted file, referred as F-IF, is utilized to speed up the procedure of multi-view matching; (3) the second inverted file (S-IF), which captures a local distribution of 3D shapes in the feature manifold, is adopted for efficient context-based re-ranking. As a result, for each query the retrieval task can be finished within one second despite the necessary cost of IO overhead. We name the proposed 3D shape search engine, which combines GPU acceleration and Inverted File Twice, as GIFT. Besides its high efficiency, GIFT also outperforms the state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks and competitions.