Learning Interpretable Representation for 3D Point Clouds

Su, Fengguang; Lin, Ci-Siang; Wang, Yu-Chiang Frank

doi:10.1109/icpr48806.2021.9412440

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Very early approaches for XAI were gradient-based saliency maps [33], class-activation maps [31,45], and some recent approaches are Layerwise relevance propagation [6]. In the field of geometric shape understanding, several researchers have attempted to extend these interpretability algorithms to 3D geometric shapes represented by voxels [12,43] or, more recently, point clouds [16,36,38,44]. However, these methods were mostly characterized by visual appearance and, in many situations, did not satisfying some of the sanity checks outlined in [2,3,18,39].…”

Section: Introductionmentioning

confidence: 99%

Concept Activation Vectors for Generating User-Defined 3D Shapes

Druc¹,

Balu²,

Wooldridge³

et al. 2022

Preprint

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We explore the interpretability of 3D geometric deep learning models in the context of Computer-Aided Design (CAD). The field of parametric CAD can be limited by the difficulty of expressing high-level design concepts in terms of a few numeric parameters. In this paper, we use a deep learning architectures to encode high dimensional 3D shapes into a vectorized latent representation that can be used to describe arbitrary concepts. Specifically, we train a simple auto-encoder to parameterize a dataset of complex shapes. To understand the latent encoded space, we use the idea of Concept Activation Vectors (CAV) to reinterpret the latent space in terms of user-defined concepts. This allows modification of a reference design to exhibit more or fewer characteristics of a chosen concept or group of concepts. We also test the statistical significance of the identified concepts and determine the sensitivity of a physical quantity of interest across the dataset.

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