Orient Anything: Learning Robust Object Orientation Estimation from
  Rendering 3D Models
Authors: name: Zehan Wang, name: Ziang Zhang, name: Tianyu Pang, name: Chao Du, name: Hengshuang Zhao, name: Zhou Zhao

Orientation is a key attribute of objects, crucial for understanding their
spatial pose and arrangement in images. However, practical solutions for
accurate orientation estimation from a single image remain underexplored. In
this work, we introduce Orient Anything, the first expert and foundational
model designed to estimate object orientation in a single- and free-view image.
Due to the scarcity of labeled data, we propose extracting knowledge from the
3D world. By developing a pipeline to annotate the front face of 3D objects and
render images from random views, we collect 2M images with precise orientation
annotations. To fully leverage the dataset, we design a robust training
objective that models the 3D orientation as probability distributions of three
angles and predicts the object orientation by fitting these distributions.
Besides, we employ several strategies to improve synthetic-to-real transfer.
Our model achieves state-of-the-art orientation estimation accuracy in both
rendered and real images and exhibits impressive zero-shot ability in various
scenarios. More importantly, our model enhances many applications, such as
comprehension and generation of complex spatial concepts and 3D object pose
adjustment.