Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

SLAM is an abbreviation for simultaneous localization and map**, which is a technique for
estimating sensor motion and reconstructing structure in an unknown environment …

We propose a direct (feature-less) monocular SLAM algorithm which, in contrast to current
state-of-the-art regarding direct methods, allows to build large-scale, consistent maps of the …

We propose a semi-direct monocular visual odometry algorithm that is precise, robust, and
faster than current state-of-the-art methods. The semi-direct approach eliminates the need of …

Obtaining high-quality 3D reconstructions of room-scale scenes is of paramount importance
for upcoming applications in AR or VR. These range from mixed reality applications for …

Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs),
this paper investigates how predicted depth maps from a deep neural network can be …

Direct methods for visual odometry (VO) have gained popularity for their capability to exploit
information from all intensity gradients in the image. However, low computational speed as …

We propose D3VO as a novel framework for monocular visual odometry that exploits deep
networks on three levels--deep depth, pose and uncertainty estimation. We first propose a …

Real-time, high-quality, 3D scanning of large-scale scenes is key to mixed reality and robotic
applications. However, scalability brings challenges of drift in pose estimation, introducing …

Augmented reality (AR) allows to seamlessly insert virtual objects in an image sequence. In
order to accomplish this goal, it is important that synthetic elements are rendered and …