Deep learning has recently gained popularity achieving state-of-the-art performance in tasks
involving text, sound, or image processing. Due to its outstanding performance, there have …

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become
important for geospatial data applications. This paper presents a comprehensive review of …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

Extracting robust and general 3D local features is key to downstream tasks such as point
cloud registration and reconstruction. Existing learning-based local descriptors are either …

Do visual tasks have a relationship, or are they unrelated? For instance, could having
surface normals simplify estimating the depth of an image? Intuition answers these …

Accurate detection of objects in 3D point clouds is a central problem in many applications,
such as autonomous navigation, housekee** robots, and augmented/virtual reality. To …

A key requirement for leveraging supervised deep learning methods is the availability of
large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very …

Although RGB-D sensors have enabled major breakthroughs for several vision tasks, such
as 3D reconstruction, we have not attained the same level of success in high-level scene …

This paper presents a local 3D descriptor for surface matching dubbed SHOT. Our proposal
stems from a taxonomy of existing methods which highlights two major approaches, referred …

A number of 3D local feature descriptors have been proposed in the literature. It is however,
unclear which descriptors are more appropriate for a particular application. A good …