Machine Learning (ML) applications on Light Detection And Ranging (LiDAR) data have
provided promising results and thus this topic has been widely addressed in the literature …

Change detection is an important step for the characterization of object dynamics at the
earth's surface. In multi-temporal point clouds, the main challenge is to detect true changes …

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 …

In recent years, the popularity of depth sensors and three-dimensional (3D) scanners has
led to the rapid development of 3D point clouds. A transformer is a type of deep neural …

Glass plays a vital role in several fields, making its accurate detection crucial. Proper
detection prevents misjudgments, reduces noise from reflections, and ensures optimal …

Glass is a common object in living environments, but detecting it can be difficult because of
the reflection and refraction of various colors of light in different environments; even humans …

Outstanding effectiveness of transformers in visual tasks has resulted in its fast growth and
adoption in three dimensions (3D) vision tasks. Vision transformers have shown numerous …

Abstract 3D reconstruction is the process of obtaining the three-dimensional shape or
surface structure of an object, which is widely used in advanced manufacturing fields such …

Remote measurement of historic buildings and structures using the technology of terrestrial
laser scanning (TLS) is becoming a more and more popular approach for conducting …

Noise removal is a critical stage in the preprocessing of point clouds, exerting a significant
impact on subsequent processes such as point cloud classification, segmentation, feature …