  Existing object pose estimation datasets are related to generic object types
and there is so far no dataset for fine-grained object categories. In this
work, we introduce a new large dataset to benchmark pose estimation for
fine-grained objects, thanks to the availability of both 2D and 3D fine-grained
data recently. Specifically, we augment two popular fine-grained recognition
datasets (StanfordCars and CompCars) by finding a fine-grained 3D CAD model for
each sub-category and manually annotating each object in images with 3D pose.
We show that, with enough training data, a full perspective model with
continuous parameters can be estimated using 2D appearance information alone.
We achieve this via a framework based on Faster/Mask R-CNN. This goes beyond
previous works on category-level pose estimation, which only estimate
discrete/continuous viewpoint angles or recover rotation matrices often with
the help of key points. Furthermore, with fine-grained 3D models available, we
incorporate a dense 3D representation named as location field into the
CNN-based pose estimation framework to further improve the performance. The new
dataset is available at www.umiacs.umd.edu/~wym/3dpose.html
