Representing Shape Collections with Alignment-Aware Linear Models
- URL: http://arxiv.org/abs/2109.01605v1
- Date: Fri, 3 Sep 2021 16:28:34 GMT
- Title: Representing Shape Collections with Alignment-Aware Linear Models
- Authors: Romain Loiseau, Tom Monnier, Lo\"ic Landrieu, Mathieu Aubry
- Abstract summary: We revisit the classical representation of 3D point clouds as linear shape models.
Our key insight is to leverage deep learning to represent a collection of shapes as affine transformations.
- Score: 17.635846912560627
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we revisit the classical representation of 3D point clouds as
linear shape models. Our key insight is to leverage deep learning to represent
a collection of shapes as affine transformations of low-dimensional linear
shape models. Each linear model is characterized by a shape prototype, a
low-dimensional shape basis and two neural networks. The networks take as input
a point cloud and predict the coordinates of a shape in the linear basis and
the affine transformation which best approximate the input. Both linear models
and neural networks are learned end-to-end using a single reconstruction loss.
The main advantage of our approach is that, in contrast to many recent deep
approaches which learn feature-based complex shape representations, our model
is explicit and every operation occurs in 3D space. As a result, our linear
shape models can be easily visualized and annotated, and failure cases can be
visually understood. While our main goal is to introduce a compact and
interpretable representation of shape collections, we show it leads to state of
the art results for few-shot segmentation.
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