Supporting multi-resolution out-of-core rendering of massive LiDAR point clouds through non-redundant data structures

This is an Accepted Manuscript of an article published by Taylor & Francis in INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE on 28 Nov 2018, available at: https://doi.org/10.1080/13658816.2018.1549734[Abstract]: In recent years, the evolution and improvement of LiDAR (Light Detection and Ranging) hardware has increased the quality and quantity of the gathered data, making the storage, processing and management thereof particularly challenging. In this work we present a novel, multi-resolution, out-of-core technique, used for web-based visualization and implemented through a non-redundant, data point organization method, which we call Hierarchically Layered Tiles (HLT), and a tree-like structure called Tile Grid Partitioning Tree (TGPT). The design of these elements is mainly focused on attaining very low levels of memory consumption, disk storage usage and network traffic on both, client and server-side, while delivering high-performance interactive visualization of massive LiDAR point clouds (up to 28 billion points) on multiplatform environments (mobile devices or desktop computers). HLT and TGPT were incorporated and tested in ViLMA (Visualization for LiDAR data using a Multi-resolution Approach), our own web-based visualization software specially designed to work with massive LiDAR point clouds.This research was supported by Xunta de Galicia under the Consolidation Programme of Competitive Reference Groups, co-founded by ERDF funds from the EU [Ref. ED431C 2017/04]; Consolidation Programme of Competitive Research Units, co-founded by ERDF funds from the EU [Ref. R2016/037]; Xunta de Galicia (Centro Singular de Investigación de Galicia accreditation 2016/2019) and the European Union (European Regional Development Fund, ERDF) under Grant [Ref. ED431G/01]; and the Ministry of Economy and Competitiveness of Spain and ERDF funds from the EU [TIN2016-75845-P].Xunta de Galicia; ED431C 2017/04Xunta de Galicia; R2016/037Xunta de Galicia; ED431G/0

Multi feature-rich synthetic colour to improve human visual perception of point clouds

Although point features have shown their usefulness in classification with Machine Learning, point cloud visualization enhancement methods focus mainly on lighting. The visualization of point features helps to improve the perception of the 3D environment. This paper proposes Multi Feature-Rich Synthetic Colour (MFRSC) as an alternative non-photorealistic colour approach of natural-coloured point clouds. The method is based on the selection of nine features (reflectance, return number, inclination, depth, height, point density, linearity, planarity, and scattering) associated with five human perception descriptors (edges, texture, shape, size, depth, orientation). The features are reduced to fit the RGB display channels. All feature permutations are analysed according to colour distance with the natural-coloured point cloud and Image Quality Assessment. As a result, the selected feature permutations allow a clear visualization of the scene's rendering objects, highlighting edges, planes, and volumetric objects. MFRSC effectively replaces natural colour, even with less distorted visualization according to BRISQUE, NIQUE and PIQE. In addition, the assignment of features in RGB channels enables the use of MFRSC in software that does not support colorization based on point attributes (most commercially available software). MFRSC can be combined with other non-photorealistic techniques such as Eye-Dome Lighting or Ambient Occlusion.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431F 2022/08Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Universidade de Vigo/CISU