Convolutional Neural Networks (CNNs) have been recently employed to solve problems from
both the computer vision and medical image analysis fields. Despite their popularity, most …

We present a method for real-time 3D object instance detection that does not require a time-consuming
training stage, and can handle untextured objects. At its core, our approach is a …

This paper addresses the problem of recognizing free-form 3D objects in point clouds.
Compared to traditional approaches based on point descriptors, which depend on local …

We present a novel method for detecting 3D model instances and estimating their 6D poses
from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover …

Importance Application of deep learning algorithms to whole-slide pathology images can
potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of …

This paper addresses the problem of estimating the depth map of a scene given a single RGB
image. We propose a fully convolutional architecture, encompassing residual learning, to …

The lack of publicly available ground-truth data has been identified as the major challenge
for transferring recent developments in deep learning to the biomedical imaging domain. …

We propose a framework for automatic modeling, detection, and tracking of 3D objects with
a Kinect. The detection part is mainly based on the recent template-based LINEMOD …

Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image
segmentation for a plethora of applications. Architectural innovations within F-CNNs have mainly …

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 …