11,329 research outputs found
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
Face modeling has been paid much attention in the field of visual computing.
There exist many scenarios, including cartoon characters, avatars for social
media, 3D face caricatures as well as face-related art and design, where
low-cost interactive face modeling is a popular approach especially among
amateur users. In this paper, we propose a deep learning based sketching system
for 3D face and caricature modeling. This system has a labor-efficient
sketching interface, that allows the user to draw freehand imprecise yet
expressive 2D lines representing the contours of facial features. A novel CNN
based deep regression network is designed for inferring 3D face models from 2D
sketches. Our network fuses both CNN and shape based features of the input
sketch, and has two independent branches of fully connected layers generating
independent subsets of coefficients for a bilinear face representation. Our
system also supports gesture based interactions for users to further manipulate
initial face models. Both user studies and numerical results indicate that our
sketching system can help users create face models quickly and effectively. A
significantly expanded face database with diverse identities, expressions and
levels of exaggeration is constructed to promote further research and
evaluation of face modeling techniques.Comment: 12 pages, 16 figures, to appear in SIGGRAPH 201
Sketch-based 3D Shape Retrieval using Convolutional Neural Networks
Retrieving 3D models from 2D human sketches has received considerable
attention in the areas of graphics, image retrieval, and computer vision.
Almost always in state of the art approaches a large amount of "best views" are
computed for 3D models, with the hope that the query sketch matches one of
these 2D projections of 3D models using predefined features.
We argue that this two stage approach (view selection -- matching) is
pragmatic but also problematic because the "best views" are subjective and
ambiguous, which makes the matching inputs obscure. This imprecise nature of
matching further makes it challenging to choose features manually. Instead of
relying on the elusive concept of "best views" and the hand-crafted features,
we propose to define our views using a minimalism approach and learn features
for both sketches and views. Specifically, we drastically reduce the number of
views to only two predefined directions for the whole dataset. Then, we learn
two Siamese Convolutional Neural Networks (CNNs), one for the views and one for
the sketches. The loss function is defined on the within-domain as well as the
cross-domain similarities. Our experiments on three benchmark datasets
demonstrate that our method is significantly better than state of the art
approaches, and outperforms them in all conventional metrics.Comment: CVPR 201
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