Abstract:
We propose a sketch-based 3D shape retrieval system that is substantially more discriminative and robust than existing systems, especially for complex models. The power of our system comes from a combination of a contourbased 2D shape representation and a robust sampling-based shape matching scheme. They are defined over discriminative local features and applicable for partial sketches; robust to noise and distortions in hand drawings; and consistent when strokes are added progressively. Our robust shape matching, however, requires dense sampling and registration and incurs a high computational cost. We thus devise critical acceleration methods to achieve interactive performance: precomputing kNN graphs that record transformations between neighboring contour images and enable fast online shape alignment; pruning sampling and shape registration strategically and hierarchically; and parallelizing shape matching on multi-core platforms or GPUs. We demonstrate the effectiveness of our system through various experiments, comparisons, and user studies.
Teaser caption: User study: (a) front-view pictures of the target models are given to users as reference. (b) Some user-drawn partial and complete sketches, which all successfully retrieved the targets using our method. (c) Three example query sketches and their corresponding retrieval results using our method. (d) The retrieval results using a search algorithm similar to [FMK.03].
System Pipeline:
 |
Video: (download)
Bibtex:
@article{ shao:discriminative,
author = {Tianjia Shao and Weiwei Xu and KangKang Yin and Jingdong Wang and Kun Zhou and Baining Guo},
title = {Discriminative Sketch-based 3D Model Retrieval via Robust Shape Matching},
journal = {Comput. Graph. Forum},
issue_date = {September 2011},
volume = {30},
number = {7},
YEAR = {2011},
pages = {2011-2020},
url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8659.2011.02050.x/abstract},
doi = {10.1111/j.1467-8659.2011.02050.x},
}
|
|
