In recent years, deep learning techniques for processing 3D point cloud data have seen
significant advancements, given their unique ability to extract relevant features and handle …

Three-dimensional point cloud classification is fundamental but still challenging in 3-D
vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and …

As the basic task of point cloud analysis, classification is fundamental but always
challenging. To address some unsolved problems of existing methods, we propose a …

3D scene graph prediction is important for intelligent agents to gather information and
perceive semantics of their environments. However, constructing an effective graph is …

Point cloud classification is a fundamental task in 3D applications. However, it is challenging
to achieve effective feature learning due to the irregularity and unordered nature of point …

Transformers have been at the heart of the Natural Language Processing (NLP) and
Computer Vision (CV) revolutions. The significant success in NLP and CV inspired exploring …

This paper introduces a new DBSCAN-based method for boundary detection and plane
segmentation for 3D point clouds. The proposed method is based on candidate samples …

3D segmentation is a fundamental and challenging problem in computer vision with
applications in autonomous driving, robotics, augmented reality and medical image …

In precision agriculture, the accurate segmentation of crops and weeds in agronomic images
has always been the center of attention. Many methods have been proposed but still the …

Point cloud semantic segmentation in urban scenes plays a vital role in intelligent city
modeling, autonomous driving, and urban planning. Point cloud semantic segmentation …