Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images
This addresses the need for more robust 3D understanding in computer vision by relaxing assumptions about object category, though it is incremental as it builds on existing CNN-based pose estimation methods.
The paper tackles the problem of jointly estimating object category and 3D pose from 2D images with known 2D localization, proposing a new architecture with shared features and category-dependent pose networks, achieving state-of-the-art performance on the PASCAL3D+ dataset and matching methods that assume known category.
Current CNN-based algorithms for recovering the 3D pose of an object in an image assume knowledge about both the object category and its 2D localization in the image. In this paper, we relax one of these constraints and propose to solve the task of joint object category and 3D pose estimation from an image assuming known 2D localization. We design a new architecture for this task composed of a feature network that is shared between subtasks, an object categorization network built on top of the feature network, and a collection of category dependent pose regression networks. We also introduce suitable loss functions and a training method for the new architecture. Experiments on the challenging PASCAL3D+ dataset show state-of-the-art performance in the joint categorization and pose estimation task. Moreover, our performance on the joint task is comparable to the performance of state-of-the-art methods on the simpler 3D pose estimation with known object category task.