The rapid development of 3D sensors and object detection methods based on 3D point
clouds has led to increasing demand for labeling tools that provide suitable training data …

Three-dimensional information perception from point clouds is of vital importance for
improving the ability of machines to understand the world, especially for autonomous driving …

Manually annotating 3D point clouds is laborious and costly, limiting the training data
preparation for deep learning in real-world object detection. While a few previous studies …

RGB-D cameras, which give an RGB image together with depths, are becoming increasingly
popular for robotic perception. In this paper, we address the task of detecting commonly …

Point cloud registration is one of the key issues in fields that need 3D scenes with global
vision, including 3D scene reconstruction in robot technology, high-accuracy 3D map …

ABSTRACT RGB-D sensors are gradually introduced into the robotic system to help the
machine understand its surroundings. Among the point cloud processing methods, instance …

Mobile robots can generate point cloud maps based on continuous image sequences to
realize the perception of the environment. However, in the lightweight application scenario …

In this paper we present a new approach for labeling 3D point clouds. We use Conditional
Random Fields (CRFs) as an objective function, with unary energy term assessing the …

Deep neural networks for 3D point cloud processing have exhibited superior performance
on many tasks. However, the structure and computational complexity of existing networks …

Within the past decade, the rise of applications based on artificial intelligence (AI) in general
and machine learning (ML) in specific has led to many significant contributions within …