In neural networks, it is often desirable to work with various representations of the same
space. For example, 3D rotations can be represented with quaternions or Euler angles. In …

This paper proposes to use the three vectors in a rotation matrix as the representation in
head pose estimation and develops a new neural network based on the characteristic of …

Facial pose estimation refers to the task of predicting face orientation from a single RGB
image. It is an important research topic with a wide range of applications in computer vision …

This paper addresses the task of estimating the 6 degrees of freedom pose of a known 3D
object from depth information represented by a point cloud. Deep features learned by …

Regressing rotations on SO (3) manifold using deep neural networks is an important yet
unsolved problem. The gap between the Euclidean network output space and the non …

Accurate object 6D pose estimation is a core task for robot bin-picking applications,
especially when objects are randomly stacked with heavy occlusion. To address this …

It is often desired to train 6D pose estimation systems on synthetic data because manual
annotation is expensive. However, due to the large domain gap between the synthetic and …

The need to estimate the orientation between frames of reference is crucial in spacecraft
navigation. Robust algorithms for this type of problem have been built by following algebraic …

We propose a method for 6DoF pose estimation of rigid objects that uses a state-of-the-art
deep learning based instance detector to segment object instances in an RGB image …

Estimating the 3DoF rotation from a single RGB image is an important yet challenging
problem. Probabilistic rotation regression has raised more and more attention with the …