Categorizing Cubes: Revisiting Pose Normalization

Abstract

This paper introduces and analyzes the novel task of categorical classification of cuboidal objects - e.g., distinguishing washing machines versus filing cabinets. To do so, it makes use of recent methods for automatic alignment of cuboidal objects in images. Given such geometric alignments, the natural approach for recognition might extract pose-normalized appearance features from a canonically-aligned coordinate frame. Though such approaches are extraordinarily common, we demonstrate that they are not optimal, both theoretically and empirically. One reason is that such approaches require accurate shape alignment. However, even with ground-truth alignment, pose-normalized representations may still be sub-optimal. Instead, we introduce methods based on pose-synthesis, a somewhat simple approach of augmenting training data with geometrically perturbed training samples. We demonstrate, both theoretically and empirically, that synthesis is a surprisingly simple but effective strategy that allows for state-of-the-art categorization and automatic 3D alignment. To aid our empirical analysis, we introduce a novel dataset for cuboidal object categorization.