4 accepted papers
High-definition (HD) maps are emerging as an essential tool for autonomous driving since they provide high-precision semantic information about the physical environment. To function as a reliable source of map information, HD maps must be constantly updated with changes that occur to the state of th…
Maps are an increasingly important tool in our daily lives, yet their rich semantic content still largely depends on manual input. Motivated by the broad availability of geo-tagged street-view images, we propose a new task aiming to make the map update process more proactive. We focus on automatical…
We propose a monocular depth estimation algorithm, which extracts a depth map from a single image, based on whole strip masking (WSM) and reliability-based refinement. First, we develop a convolutional neural network (CNN) tailored for the depth estimation. Specifically, we design a novel filter, ca…
We propose a deep learning algorithm for single-image depth estimation based on the Fourier frequency domain analysis. First, we develop a convolutional neural network structure and propose a new loss function, called depth-balanced Euclidean loss, to train the network reliably for a wide range of d…