You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

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

This paper investigates the viability of using synthetic point clouds generated from building
information models (BIMs) to train deep neural networks to perform semantic segmentation …

Deep learning in computer vision has achieved great success with the price of large-scale
labeled training data. However, exhaustive data annotation is impracticable for each task of …

Semantic segmentation using machine learning and computer vision techniques is one of
the most popular topics in autonomous driving-related research. With the revolution of deep …

Smart robots in factories highly depend on Computer Vision (CV) tasks, eg object detection
and recognition, to perceive their surroundings and react accordingly. These CV tasks can …

This paper presents a domain adaptation strategy to efficiently train network architectures for
estimating the relative camera pose in multi-view scenarios. The network architectures are …

Accurate estimation of the relative pose between an object and a robot hand is critical for
many manipulation tasks. However, most of the existing object-in-hand pose datasets use …

Plant roots are the main conduit to its interaction with the physical and biological
environment. A 3D root system architecture can provide fundamental and applied …

Obtaining annotated data for proper training of AI image classifiers remains a challenge for
successful deployment in industrial settings. As a promising alternative to handcrafted …