  Understanding and extracting 3D information of objects from monocular 2D
images is a fundamental problem in computer vision. In the task of 3D object
pose estimation, recent data driven deep neural network based approaches suffer
from scarcity of real images with 3D keypoint and pose annotations. Drawing
inspiration from human cognition, where the annotators use a 3D CAD model as
structural reference to acquire ground-truth viewpoints for real images; we
propose an iterative Semantic Pose Alignment Network, called iSPA-Net. Our
approach focuses on exploiting semantic 3D structural regularity to solve the
task of fine-grained pose estimation by predicting viewpoint difference between
a given pair of images. Such image comparison based approach also alleviates
the problem of data scarcity and hence enhances scalability of the proposed
approach for novel object categories with minimal annotation. The fine-grained
object pose estimator is also aided by correspondence of learned spatial
descriptor of the input image pair. The proposed pose alignment framework
enjoys the faculty to refine its initial pose estimation in consecutive
iterations by utilizing an online rendering setup along with effectiveness of a
non-uniform bin classification of pose-difference. This enables iSPA-Net to
achieve state-of-the-art performance on various real image viewpoint estimation
datasets. Further, we demonstrate effectiveness of the approach for multiple
applications. First, we show results for active object viewpoint localization
to capture images from similar pose considering only a single image as pose
reference. Second, we demonstrate the ability of the learned semantic
correspondence to perform unsupervised part-segmentation transfer using only a
single part-annotated 3D template model per object class. To encourage
reproducible research, we have released the codes for our proposed algorithm.
