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Communication Dans Un Congrès Année : 2021

Representing Shape Collections with Alignment-Aware Linear Models

Résumé

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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Dates et versions

hal-03487329 , version 1 (17-12-2021)

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Romain Loiseau, Tom Monnier, Mathieu Aubry, Loïc Landrieu. Representing Shape Collections with Alignment-Aware Linear Models. International Conference on 3D Vision 2021 (3DV 2021), Dec 2021, Londres (On-line), United Kingdom. ⟨hal-03487329⟩
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