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

Despite the remarkable progresses made in deep learning based depth map super-
resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps …

In this work we present a novel approach for single depth map super-resolution. Modern
consumer depth sensors, especially Time-of-Flight sensors, produce dense depth …

This paper presents a novel algorithm for estimating stereo disparity which exploits the
benefit of learning to the fullest. Given a cost volume of stereo matching, we solve the cost …

The use of multiple sensors for ego-motion estimation is an approach often used to provide
more accurate and robust results. However, when representing ego-motion as a discrete …

This paper presents an end-to-end multi-scale deep encoder (convolution) and decoder
(deconvolution) network for single image super-resolution (SISR) guided by phase …

The research into autonomous driving applications has observed an increase in computer
vision-based approaches in recent years. In attempts to develop exclusive vision-based …

This letter proposes three-filters-to-normal (3F2N), an accurate and ultrafast surface normal
estimator (SNE), which is designed for structured range sensor data, eg, depth/disparity …

We propose a direct monocular SLAM algorithm based on the Normalised Information
Distance (NID) metric. In contrast to current state-of-the-art direct methods based on …

Deep convolutional neural network (DCNN) has been successfully applied to depth map
super-resolution and outperforms existing methods by a wide margin. However, there still …