Laser radar point cloud data 3D display enhancement method and system

Abstract

The invention provides a laser radar point cloud data 3D display enhancement method and system. The laser radar point cloud data 3D display enhancement method comprises the steps of M1, performing smoothing processing on gray values of point cloud data and gray values of point cloud data in a 3D neighborhood; M2, calculating a gamma value by utilizing the gray value of the point cloud data subjected to smoothing processing; and M3, performing point cloud data gamma transformation by utilizing the calculated gamma value to realize effective adjustment of the local contrast. The method and system can be applied to 3D display of the laser radar point cloud data, the visual effect of 3D display of the point cloud data is remarkably improved by adaptively adjusting the local contrast, and visual observation of the point cloud data is facilitated.

Description

technical field [0001] The invention relates to the field of display technology, in particular to a method for enhancing 3D display of lidar point cloud data. Background technique [0002] Point cloud data means that the scanned data is recorded in the form of points, and each point contains three-dimensional coordinates. Lidar point cloud data also contains intensity information, which is the echo intensity collected by the laser scanner receiving device. This intensity information is related to the surface material, roughness, incident angle and direction of the target, as well as the emission energy of the instrument and the laser wavelength. [0003] When the lidar point cloud data is displayed in 3D, the three-dimensional coordinates of each point determine the specific position of the point, and the intensity information is converted into a 255-level gray value as the gray value of the point. Due to the large intensity range of different point cloud data in the scene,...

AI Technical Summary

Problems solved by technology

Due to the large intensity range of different point cloud data in the scene, converting the intensity value range to the gray value range of 0 to 255 will result in poor contrast in local areas, and the features of interest will not be obvious, affecting the overall visual effect

For example, in the autonomous driving scene, the point cloud intensity of the general traffic sign area is over 100, and the object area with high reflectivity of some materials and close distances even reaches over 200; while the point cloud intensity of the road surface area is below 10, the point cloud intensity of the lane line area is less than 100. It is around 20; if the range of intensity 0-255 is mapped to the range of gray value 0-255 for display, then the contrast between the lane line area and the road surface area we are interested in will be very poor, which is not conducive to observation

[0004] In order to improve the above situation, there is also a partial interception of the intensity value range, and then convert the intercepted part to the gray value range of 0 to 255 for display, but this will cause some point cloud data (intensity exceeding the intercepted part) to lose contrast when displayed.

For example, in the above example, the interception intensity of point cloud data ranges from 0 to 40. At this time, the contrast between the lane line area and the road surface area is excellent, but the traffic sign area will lose contrast.