Artificial perception for robots operating in outdoor natural environments, including forest
scenarios, has been the object of a substantial amount of research for decades. Regardless …

Existing deep learning-based depth completion methods generally employ massive stacked
layers to predict the dense depth map from sparse input data. Although such approaches …

Depth completion aims to recover pixelwise depth from incomplete and noisy depth
measurements with or without the guidance of a reference RGB image. This task attracted …

Simulation has the potential to transform the development of robust algorithms for mobile
agents deployed in safety-critical scenarios. However, the poor photorealism and lack of …

We present a method for inferring dense depth from a camera image and a sparse noisy
radar point cloud. We first describe the mechanics behind mmWave radar point cloud …

Projecting the point cloud on the 2D spherical range image transforms the LiDAR semantic
segmentation to a 2D segmentation task on the range image. However, the LiDAR range …

We present a novel depth completion approach agnostic to the sparsity of depth points, that
is very likely to vary in many practical applications. State-of-the-art approaches yield …

Remarkable progress has been achieved by current depth completion approaches, which
produce dense depth maps from sparse depth maps and corresponding color images …

Depth completion aims to derive a dense depth map from sparse depth measurements with
a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly …

This paper proposes a new framework for depth completion robust against domain-shifting
issues. It exploits the generalization capability of modern stereo networks to face depth …