Descriptors play an important role in point cloud registration. The current state-of-the-art resorts to the high regression capability of deep learning. However, recent learning-based descriptors require different levels annotation and selection patches, which make model hard migrate new scenarios. In this work, we learn local registration for clouds a self-supervised manner. each iteration tra...

The Iterative Closest Points (ICP) algorithm is the mainstream algorithm used in the process of accurate registration of 3D point cloud data. The algorithm requires a proper initial value and the approximate registration of two point clouds to prevent the algorithm from falling into local extremes, but in the actual point cloud matching process, it is difficult to ensure compliance with this re...

In recent years, three-dimensional imaging techniques have become an increasingly active research area. One of the most common outputs from a three-dimensional imaging system is a point cloud, a collection of points in three-dimensional space. Many techniques exist for generating point clouds, including image-based approaches and LIght Detection And Ranging (Lidar). Point clouds have become inc...

In this paper we show how using histograms based on the angular relationships between a subset of point normals in a 3D point Cloud can be used in a machine learning algorithm in order to recognize different classes of objects given by their 3D point clouds. This approach extends the work done by Gary Bradski at Willow Garage on point clouds recognition by applying a machine learning approach t...

Since devices to capture point clouds easily are relatively recent (Kinect), there has not been much research into segmenting out objects from a point cloud. Previous work in the segmentation of 3d point cloud scenes has usually involved the extracting geometric primitives using features like normals and curvatures [2, 3]. Other research has focused on segmenting out a single object foreground ...

Three-dimensional (3D) point cloud models of trees were developed using the software packages Maya and MATLAB. These models are to be assembled into a forest scene that can be used for producing simulated Laser Detection and Ranging (LADAR) imagery of objects obscured beneath the canopy. This document describes the approach for modelling the trees using Maya then importing and completely recons...

Terrain point cloud data are typically acquired through some form of Light Detection And Ranging sensing. They form a rich resource that is important in a variety of applications including navigation, line of sight, and terrain visualization. Processing terrain data has not received the attention of other forms of surface reconstruction or of image processing. The goal of terrain data processin...

This contribution describes recent development in ongoing work focused on point cloud rendering algorithm implementation usable in environments containing programmable or custom hardware. The approach described in this paper is based on the idea that direct point cloud rendering, which is in the principle not too complicated, can be efficiently implemented in programmable or custom hardware. Su...