13,294 research outputs found
Sparse data driven mesh deformation
Example-based mesh deformation methods are powerful tools for realistic shape editing. However, existing techniques typically combine all the example deformation modes, which can lead to overfitting, i.e. using an overly complicated model to explain the user-specified deformation. This leads to implausible or unstable deformation results, including unexpected global changes outside the region of interest. To address this fundamental limitation, we propose a sparse blending method that automatically selects a smaller number of deformation modes to compactly describe the desired deformation. This along with a suitably chosen deformation basis including spatially localized deformation modes leads to significant advantages, including more meaningful, reliable, and efficient deformations because fewer and localized deformation modes are applied. To cope with large rotations, we develop a simple but effective representation based on polar decomposition of deformation gradients, which resolves the ambiguity of large global rotations using an as-consistent-as-possible global optimization. This simple representation has a closed form solution for derivatives, making it efficient for our sparse localized representation and thus ensuring interactive performance. Experimental results show that our method outperforms state-of-the-art data-driven mesh deformation methods, for both quality of results and efficiency
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
3D reconstruction from a single image is a key problem in multiple
applications ranging from robotic manipulation to augmented reality. Prior
methods have tackled this problem through generative models which predict 3D
reconstructions as voxels or point clouds. However, these methods can be
computationally expensive and miss fine details. We introduce a new
differentiable layer for 3D data deformation and use it in DeformNet to learn a
model for 3D reconstruction-through-deformation. DeformNet takes an image
input, searches the nearest shape template from a database, and deforms the
template to match the query image. We evaluate our approach on the ShapeNet
dataset and show that - (a) the Free-Form Deformation layer is a powerful new
building block for Deep Learning models that manipulate 3D data (b) DeformNet
uses this FFD layer combined with shape retrieval for smooth and
detail-preserving 3D reconstruction of qualitatively plausible point clouds
with respect to a single query image (c) compared to other state-of-the-art 3D
reconstruction methods, DeformNet quantitatively matches or outperforms their
benchmarks by significant margins. For more information, visit:
https://deformnet-site.github.io/DeformNet-website/ .Comment: 11 pages, 9 figures, NIP
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
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