• Corpus ID: 219792402

UV-Net: Learning from Curve-Networks and Solids

@article{Jayaraman2020UVNetLF,
  title={UV-Net: Learning from Curve-Networks and Solids},
  author={Pradeep Kumar Jayaraman and Aditya Sanghi and J. Lambourne and T. Davies and Hooman Shayani and Nigel Morris},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.10211}
}
Parametric curves, surfaces and boundary representations are the basis for 2D vector graphics and 3D industrial designs. Despite their prevalence, there exists limited research on applying modern deep neural networks directly to such representations. The unique challenges in working with such representations arise from the combination of continuous non-Euclidean geometry domain and discrete topology, as well as a lack of labeled datasets, benchmarks and baseline models. In this paper, we… 

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References

SHOWING 1-10 OF 38 REFERENCES
Deep Learning 3D Shape Surfaces Using Geometry Images
TLDR
This work qualitatively and quantitatively validate that creating geometry images using authalic parametrization on a spherical domain is suitable for robust learning of 3D shape surfaces, and proposes a way to implicitly learn the topology and structure of3D shapes using geometry images encoded with suitable features.
Shape Reconstruction by Learning Differentiable Surface Representations
TLDR
This paper shows that the inherent differentiability of deep networks can be exploited to leverage differential surface properties during training so as to prevent patch collapse and strongly reduce patch overlap, and this lets us reliably compute quantities such as surface normals and curvatures.
ABC: A Big CAD Model Dataset for Geometric Deep Learning
TLDR
This work performs a large-scale benchmark for estimation of surface normals, comparing existing data driven methods and evaluating their performance against both the ground truth and traditional normal estimation methods.
DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces
TLDR
This work proposes a deep learning architecture that adapts to perform spline fitting tasks accordingly, providing complementary results to the aforementioned traditional methods.
Deep Parametric Shape Predictions Using Distance Fields
TLDR
This work uses distance fields to transition between shape parameters like control points and input data on a pixel grid and demonstrates efficacy on 2D and 3D tasks, including font vectorization and surface abstraction.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
TLDR
This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.
Learning Representations and Generative Models for 3D Point Clouds
TLDR
A deep AutoEncoder network with state-of-the-art reconstruction quality and generalization ability is introduced with results that outperform existing methods on 3D recognition tasks and enable shape editing via simple algebraic manipulations.
CubeNet: Equivariance to 3D Rotation and Translation
TLDR
A Group Convolutional Neural Network with linear equivariance to translations and right angle rotations in three dimensions is introduced, and is believed to be the first 3D rotation equivariant CNN for voxel representations.
AtlasNet: A Papier-M\^ach\'e Approach to Learning 3D Surface Generation
TLDR
A method for learning to generate the surface of 3D shapes as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape.
Geometric Deep Learning: Going beyond Euclidean data
TLDR
Deep neural networks are used for solving a broad range of problems from computer vision, natural-language processing, and audio analysis where the invariances of these structures are built into networks used to model them.
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