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.

We introduce a new approach for recognizing and reconstructing 3D objects in images. Our approach is based on an analysis by synthesis strategy. A forward synthesis model constructs possible geometric interpretations of the world, and then selects the interpretation that best agrees with the measured visual evidence. The forward model synthesizes visual templates defined on invariant (HOG) features. These visual templates are discriminatively trained to be accurate for inverse estimation. We introduce an efficient "brute-force" approach to inference that searches through a large number of candidate reconstructions, returning the optimal one. One benefit of such an approach is that recognition is inherently (re)constructive. We show state of the art performance for detection and reconstruction on two challenging 3D object recognition datasets of cars and cuboids.

We present an approach to detecting and analyzing the 3D configuration of objects in real-world images with heavy occlusion and clutter. We focus on the application of finding and analyzing cars. We do so with a two-stage model; the first stage reasons about 2D shape and appearance variation due to within-class variation(station wagons look different than sedans) and changes in viewpoint. Rather than using a view-based model, we describe a compositional representation that models a large number of effective views and shapes using a small number of local view-based templates. We use this model to propose candidate detections and 2D estimates of shape. These estimates are then refined by our second stage, using an explicit 3D model of shape and viewpoint. We use a morphable model to capture 3D within-class variation, and use a weak-perspective camera model to capture viewpoint. We learn all model parameters from 2D annotations. We demonstrate state-of-the-art accuracy for detection, viewpoint estimation, and 3D shape reconstruction on challenging images from the PASCAL VOC 2011 dataset.