With the fast development of 3D data acquisition techniques, topographic point clouds have
become easier to acquire and have promoted many geospatial applications. Ground filtering …

Brain-inspired algorithms have become a new trend in next-generation artificial intelligence.
Through research on brain science, the intelligence of remote sensing algorithms can be …

An essential prerequisite for unleashing the potential of supervised deep learning
algorithms in the area of 3D scene understanding is the availability of large-scale and richly …

With the rapid advancement of 3D sensors, there is an increasing demand for 3D scene
understanding and an increasing number of 3D deep learning algorithms have been …

Although various 3D datasets with different functions and scales have been proposed
recently, it remains challenging for individuals to complete the whole pipeline of large-scale …

With the recent availability and affordability of commercial depth sensors and 3D scanners,
an increasing number of 3D (ie, RGBD, point cloud) datasets have been publicized to …

The classification of airborne laser scanning (ALS) point clouds is a critical task of remote
sensing and photogrammetry fields. Although recent deep learning-based methods have …

In this paper we present LiDAR-Net a new real-scanned indoor point cloud dataset
containing nearly 3.6 billion precisely point-level annotated points covering an expansive …

Kernel point convolution (KPConv) can effectively represent the point features of point cloud
data. However, KPConv-based methods just consider the local information of each point …

Although novel point cloud semantic segmentation schemes that continuously surpass state-
of-the-art results exist, the success of learning an effective model typically relies on the …