Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …

Loop closure detection is an essential component of simultaneous localization and mapping
(SLAM) systems, which reduces the drift accumulated over time. Over the years, several …

Point clouds are a widely available and canonical data modality which convey the 3D
geometry of a scene. Despite significant progress in classification and segmentation from …

Single image pose estimation is a fundamental problem in many vision and robotics tasks,
and existing deep learning approaches suffer by not completely modeling and handling: i) …

Modern deep learning techniques that regress the relative camera pose between two
images have difficulty dealing with challenging scenarios, such as large camera motions …

Symmetric orthogonalization via SVD, and closely related procedures, are well-known
techniques for projecting matrices onto O (n) or SO (n). These tools have long been used for …

Autonomous systems and humans are increasingly sharing the same space. Robots work
side by side or even hand in hand with humans to balance each other's limitations. Such …

In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can
naturally handle pose-related uncertainties and ambiguities arising in almost all real life …

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 …