Monocular depth estimation using learning-based approaches has become promising in
recent years. However, most monocular depth estimators either need to rely on large …

Despite significant research focus on 3D scene capture systems, numerous unresolved
challenges remain in relation to achieving full coverage scene depth estimation which is the …

Abstract Semantic Scene Completion (SSC) aims to simultaneously predict the volumetric
occupancy and semantic category of a 3D scene. It helps intelligent devices to understand …

We introduce a View-Volume convolutional neural network (VVNet) for inferring the
occupancy and semantic labels of a volumetric 3D scene from a single depth image. The …

Robust geometric and semantic scene understanding is ever more important in many real-
world applications such as autonomous driving and robotic navigation. In this paper, we …

Pseudo-LiDAR point cloud interpolation is a novel and challenging task in autonomous
driving, which aims to address the frequency mismatching problem between a camera and a …

Diminished reality (DR) refers to the removal of real objects from the environment by virtually
replacing them with their background. Modern DR frameworks use inpainting to hallucinate …

In this work, the issue of depth filling is addressed using a self-supervised feature learning
model that predicts missing depth pixel values based on the context and structure of the …

This paper addresses the problem of uploading large quantities of sensitive industrial data
to a public distributed network by proposing a new framework. The framework combines the …

Monocular depth estimation using novel learning-based approaches has recently emerged
as a promising potential alternative to more conventional 3D scene capture technologies …