  Existing 3D pose datasets of object categories are limited to generic object
types and lack of fine-grained information. In this work, we introduce a new
large-scale dataset that consists of 409 fine-grained categories and 31,881
images with accurate 3D pose annotation. Specifically, we augment three
existing fine-grained object recognition datasets (StanfordCars, CompCars and
FGVC-Aircraft) by finding a specific 3D model for each sub-category from
ShapeNet and manually annotating each 2D image by adjusting a full set of 7
continuous perspective parameters. Since the fine-grained shapes allow 3D
models to better fit the images, we further improve the annotation quality by
initializing from the human annotation and conducting local search of the pose
parameters with the objective of maximizing the IoUs between the projected mask
and the segmentation reference estimated from state-of-the-art deep
Convolutional Neural Networks (CNNs). We provide full statistics of the
annotations with qualitative and quantitative comparisons suggesting that our
dataset can be a complementary source for studying 3D pose estimation. The
dataset can be downloaded at http://users.umiacs.umd.edu/~wym/3dpose.html.
