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.Comment: accepted by CVPR16, achieved the first place in Shrec2016
competition: Large-Scale 3D Shape Retrieval under the perturbed cas
