The ability to predict, anticipate and reason about future outcomes is a key component of
intelligent decision-making systems. In light of the success of deep learning in computer …

Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …

Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple
learning algorithms. Many regress precise geometric quantities, like poses or 3D points …

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 …

We introduce GNeRF, a framework to marry Generative Adversarial Networks (GAN) with
Neural Radiance Field (NeRF) reconstruction for the complex scenarios with unknown and …

Robust and accurate visual localization is a fundamental capability for numerous
applications, such as autonomous driving, mobile robotics, or augmented reality. It remains …

DoF object pose estimation from a single RGB image is a fundamental and long-standing
problem in computer vision. Current leading approaches solve it by training deep networks …

We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical
flow and ego-motion estimation from videos. The three components are coupled by the …

Estimating the 6D pose of objects from images is an important problem in various
applications such as robot manipulation and virtual reality. While direct regression of images …

Visual localization is the task of accurate camera pose estimation in a known scene. It is a
key problem in computer vision and robotics, with applications including self-driving cars …