A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

•Build a large-scale 3D shape retrieval benchmark that supports multi-modal queries.•Evaluate the 26 3D shape retrieval methods using 3 types of metrics.•Solicit and identify state-of-the-art methods and promising related techniques.•Perform detailed analysis on diverse methods w.r.t accuracy and ef...

Full description

Saved in:
Bibliographic Details
Published in:Computer vision and image understanding 2015-02, Vol.131, p.1-27
Main Authors: Li, Bo, Lu, Yijuan, Li, Chunyuan, Godil, Afzal, Schreck, Tobias, Aono, Masaki, Burtscher, Martin, Chen, Qiang, Chowdhury, Nihad Karim, Fang, Bin, Fu, Hongbo, Furuya, Takahiko, Li, Haisheng, Liu, Jianzhuang, Johan, Henry, Kosaka, Ryuichi, Koyanagi, Hitoshi, Ohbuchi, Ryutarou, Tatsuma, Atsushi, Wan, Yajuan, Zhang, Chaoli, Zou, Changqing
Format: Article
Language:English
Subjects:
3D shape retrieval
Algorithms
Benchmarking
Computer vision
Large-scale benchmark
Multimodal queries
Performance evaluation
Queries
Query-by-Model
Query-by-Sketch
Retrieval
SHREC
Sketches
State of the art
Three dimensional
Three dimensional models
Unified
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Build a large-scale 3D shape retrieval benchmark that supports multi-modal queries.•Evaluate the 26 3D shape retrieval methods using 3 types of metrics.•Solicit and identify state-of-the-art methods and promising related techniques.•Perform detailed analysis on diverse methods w.r.t accuracy and efficiency.•Make benchmark and evaluation tools freely available to the community. Large-scale 3D shape retrieval has become an important research direction in content-based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on large scale comprehensive and sketch-based 3D model retrieval have been organized by us in 2014. Both tracks were based on a unified large-scale benchmark that supports multimodal queries (3D models and sketches). This benchmark contains 13680 sketches and 8987 3D models, divided into 171 distinct classes. It was compiled to be a superset of existing benchmarks and presents a new challenge to retrieval methods as it comprises generic models as well as domain-specific model types. Twelve and six distinct 3D shape retrieval methods have competed with each other in these two contests, respectively. To measure and compare the performance of the participating and other promising Query-by-Model or Query-by-Sketch 3D shape retrieval methods and to solicit state-of-the-art approaches, we perform a more comprehensive comparison of twenty-six (eighteen originally participating algorithms and eight additional state-of-the-art or new) retrieval methods by evaluating them on the common benchmark. The benchmark, results, and evaluation tools are publicly available at our websites (http://www.itl.nist.gov/iad/vug/sharp/contest/2014/Generic3D/, 2014, http://www.itl.nist.gov/iad/vug/sharp/contest/2014/SBR/, 2014).
ISSN:1077-3142
1090-235X
DOI:10.1016/j.cviu.2014.10.006