Evaluation of Latent Space Learning With Procedurally-Generated Datasets of Shapes

Sharjeel Ali, Oliver van Kaick; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 2086-2094

We compare the quality of latent spaces learned by different neural network models for organizing collections of 3D shapes. To accomplish this goal, our first contribution is to introduce a synthetic dataset of shapes with known semantic attributes. We use a procedural method to generate a dataset comprising four categories, with a total of over 10,000 shapes, providing a controlled setting for studying the properties of latent spaces. In contrast to previous work, the synthetic shapes generated with our method have a more realistic appearance, similar to objects in manually-modeled collections. We use 8,800 shapes from the generated dataset to perform a quantitative and qualitative evaluation of the latent spaces learned with a set of representative neural network models. Our second contribution is to perform the quantitative evaluation with measures that we developed for numerically assessing the properties of the latent spaces, which allow us to objectively compare different models based on statistics computed on large sets of shapes.