Depth estimation is a classic task in computer vision, which is of great significance for many
applications such as augmented reality, target tracking and autonomous driving. Traditional …

Segmentation has been a rooted area of research having diverse dimensions. The roots of
image segmentation and its associated techniques have supported computer vision, pattern …

Depth completion aims to recover a dense depth map from the sparse depth data and the
corresponding single RGB image. The observed pixels provide the significant guidance for …

Multitask joint learning technology continues gaining more attention as a paradigm shift and
has shown promising performance in many applications. Depth estimation and semantic …

Depth completion recovers dense depth from sparse measurements, eg, LiDAR. Existing
depth-only methods use sparse depth as the only input. However, these methods may fail to …

In recent years, the development of convolutional neural networks (CNNs) has promoted
continuous progress in scene classification of remote sensing images. Compared with …

Depth estimation from a camera is an important task for 3D perception. Recently, without
using the labeled ground truth of depth map, a self-supervised deep learning network can …

Accurate real depth annotations are difficult to acquire, needing the use of special devices
such as a LiDAR sensor. Self-supervised methods try to overcome this problem by …

Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years,
annotation remains expensive and time-consuming, leading to a demand for semi …

The three-dimensional (3D) perception of autonomous vehicles is crucial for localization and
analysis of the driving environment, while it involves massive computing resources for deep …