Multispectral LiDAR point cloud highlight removal based on color information

Abstract

With the rapid development of light detection and ranging (LiDAR) technology, multispectral LiDAR (MSL) can realize three-dimensional (3D) imaging of the ground object by acquiring rich spectral information. Although color restoration has been achieved on the basis of the full-waveform data of MSL, further improvement of the visual effect of color point clouds still faces many challenges. In this paper, a highlight removal method for MSL color point clouds is proposed to explore the potential of 3D visualization. First, the MSL reflection model are introduced according to radar equation and Phong model, and the restored color of the MSL point clouds is determined to comprise diffuse and specular components. Second, a data conversion method is proposed to improve the massive point cloud processing efficiency by spatial dimension reduction and data compression. Then, the visual saliency map after color denoising is used to obtain the highlight region, the unknown information of which is recovered based on the global or local color information. Finally, three representative targets are selected and evaluated by qualitative and quantitative validation, which verifies that the method can effectively recover the high-quality highlight-free point clouds of MSL.