Multispectral imaging and deep learning have emerged as powerful tools supporting diverse
use cases from autonomous vehicles to agriculture, infrastructure monitoring and …

Objective: Deep learning-based object recognition, 6D pose estimation, and semantic scene
understanding require a large amount of training data to achieve generalization. Time …

Solving the camera-to-robot pose is a fundamental requirement for vision-based robot
control, and is a process that takes considerable effort and cares to make accurate …

Simultaneously accurate and reliable tracking control for quadrotors in complex dynamic
environments is challenging. The chaotic nature of aerodynamics, derived from drag forces …

This paper is about effectively utilizing synthetic data for training deep neural networks for
industrial parts classification, in particular, by taking into account the domain gap against …

Without the demand of training in reality, humans can easily detect a known concept simply
based on its language description. Empowering deep learning with this ability undoubtedly …

Autonomously navigating robots need to perceive and interpret their surroundings.
Currently, cameras are among the most used sensors due to their high resolution and frame …

Unsupervised domain-adaptive object detection uses labeled source domain data and
unlabeled target domain data to alleviate the domain shift and reduce the dependence on …

Despite the recent success of Neural Radiance Field (NeRF), it is still challenging to render
large-scale driving scenes with long trajectories, particularly when the rendering quality and …

This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a
domain adaptation problem. In this case, we present a global-to-local method to address …