Despite the fact that object detection, 3D pose estima-tion, and sub-category recognition are highly correlated tasks, they are usually addressed independently from each other because of the huge space of parameters. To jointly model all of these tasks, we propose a coarse-to-fine hier-archical representation, where each level of the hierarchy represents objects at a different level of granularity. The hi-erarchical representation prevents performance loss, which is often caused by the increase in the number of parameters (as we consider more tasks to model), and the joint model-ing enables resolving ambiguities that exist in independent modeling of these tasks. We augment PASCAL3D+ [34] dataset with annotations for these tasks and show that our hierarchical model is effective in joint modeling of object detection, 3D pose estimation, and sub-category recogni-tion. 1.

Object recognition approaches have recently been ex-tended to yield, aside of the object class output, also view-point or pose. Training such approaches typically requires additional viewpoint or keypoint annotation in the training data or, alternatively, synthetic CAD models. In this paper, we present an approach that creates a dataset of images an-notated with bounding boxes and viewpoint labels in a fully automated manner from videos. We assume that the scene is static in order to reconstruct 3D surfaces via structure from motion. We automatically detect when the reconstruc-tion fails and normalize for the viewpoint of the 3D models by aligning the reconstructed point clouds. Exemplarily for cars we show that we can expand a large dataset of anno-tated single images and obtain improved performance when training a viewpoint regressor on this joined dataset. 1.

Abstract. Man-made objects, such as chairs, often have very large shape varia-tions, making it challenging to detect them. In this work we investigate the task of finding particular object shapes from a single depth image. We tackle this task by exploiting the inherently low dimensionality in the object shape variations, which we discover and encode as a compact shape space. Starting from any col-lection of 3D models, we first train a low dimensional Gaussian Process Latent Variable Shape Space. We then sample this space, effectively producing infinite amounts of shape variations, which are used for training. Additionally, to support fast and accurate inference, we improve the standard 3D object category proposal generation pipeline by applying a shallow convolutional neural network-based fil-tering stage. This combination leads to considerable improvements for proposal generation, in both speed and accuracy. We compare our full system to previ-ous state-of-the-art approaches, on four different shape classes, and show a clear improvement. 1