The3D object detection of LiDAR point cloud data has generated widespread discussion and implementation in recent years. In this paper, we concentrate on exploring the sampling method point-based 3D autonomous driving scenarios, a process which attempts to reduce expenditure by reaching sufficient accuracy using fewer selected points. FPS (farthest sampling), most used method, works poorly smal...

DEFINITION Given a set of n points and a query point, q, the nearest-neighbor problem is concerned with finding the point closest to the query point. Figure 1 shows an example of the nearest neighbor problem. On the left side is a set of n = 10 points in a two-dimensional space with a query point, q. The right shows the problem solution, s. Figure 1: An example of a nearest-neighbor problem dom...

We present an algorithm to compute the geodesic $$L_1$$ farthest-point Voronoi diagram of m point sites in presence n rectangular obstacles plane. It takes $$O(nm+n \log + m\log m)$$ construction time using O(nm) space. This is first optimal for constructing obstacles. can construct a data structure same and space that answers farthest-neighbor query $$O(\log (n+m))$$ time.

Semantic segmentation in LiDAR point clouds has become an important research topic for autonomous driving systems. This paper proposes a dynamic graph convolution neural network cloud semantic using polar bird’s-eye view, referred to as DGPolarNet. are converted coordinates, which rasterized into regular grids. The points mapped onto each grid distribute evenly solve the problem of sparse distr...

Despite ample research, understanding plant spread and predicting their ability to track projected climate changes remain a formidable challenge to be confronted. We modelled the spread of North American wind-dispersed trees in current and future (c. 2060) conditions, accounting for variation in 10 key dispersal, demographic and environmental factors affecting population spread. Predicted sprea...

The traditional K-means clustering algorithm is difficult to initialize the number of clusters K, and the initial cluster centers are selected randomly, this makes the clustering results very unstable. Meanwhile, algorithms are susceptible to noise points. To solve the problems, the traditional K-means algorithm is improved. The improved method is divided into the same grid in space, according ...

In this paper, we present a novel method for surface sampling and remeshing with good blue-noise properties. Our approach is based on the farthest point optimization (FPO), a relaxation technique that generates high quality blue-noise point sets in 2D. We propose two important generalizations of the original FPO framework: adaptive sampling and sampling on surfaces. A simple and efficient algor...