Point2Mesh: A Self-Prior for Deformable Meshes
- URL: http://arxiv.org/abs/2005.11084v1
- Date: Fri, 22 May 2020 10:01:04 GMT
- Title: Point2Mesh: A Self-Prior for Deformable Meshes
- Authors: Rana Hanocka, Gal Metzer, Raja Giryes, Daniel Cohen-Or
- Abstract summary: We introduce Point2Mesh, a technique for reconstructing a surface mesh from an input point cloud.
The self-prior encapsulates reoccurring geometric repetitions from a single shape within the weights of a deep neural network.
We show that Point2Mesh converges to a desirable solution; compared to a prescribed smoothness prior, which often becomes trapped in undesirable local minima.
- Score: 83.31236364265403
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we introduce Point2Mesh, a technique for reconstructing a
surface mesh from an input point cloud. Instead of explicitly specifying a
prior that encodes the expected shape properties, the prior is defined
automatically using the input point cloud, which we refer to as a self-prior.
The self-prior encapsulates reoccurring geometric repetitions from a single
shape within the weights of a deep neural network. We optimize the network
weights to deform an initial mesh to shrink-wrap a single input point cloud.
This explicitly considers the entire reconstructed shape, since shared local
kernels are calculated to fit the overall object. The convolutional kernels are
optimized globally across the entire shape, which inherently encourages
local-scale geometric self-similarity across the shape surface. We show that
shrink-wrapping a point cloud with a self-prior converges to a desirable
solution; compared to a prescribed smoothness prior, which often becomes
trapped in undesirable local minima. While the performance of traditional
reconstruction approaches degrades in non-ideal conditions that are often
present in real world scanning, i.e., unoriented normals, noise and missing
(low density) parts, Point2Mesh is robust to non-ideal conditions. We
demonstrate the performance of Point2Mesh on a large variety of shapes with
varying complexity.
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