Coarse-grained multiresolution structures for mobile exploration of gigantic surface models

Abstract

Figure 1: Top row: Giga-triangle models interactively explored on a iPhone 4 and on "the new iPad" using the Compact Adaptive TetraPuzzles (CATP) approach. Bottom row: Parameterized scanned models interactively explored using the Adaptive Quad Patches (AQP) approach on a Acer Iconia Tab W501 using native OpenGL implementation (two leftmost images) and on a linux laptop using a WebGL/JavaScript running within Chromium (two rightmost images). AbstractWe discuss our experience in creating scalable systems for distributing and rendering gigantic 3D surfaces on web environments and common handheld devices. Our methods are based on compressed streamable coarse-grained multiresolution structures. By combining CPU and GPU compression technology with our multiresolution data representation, we are able to incrementally transfer, locally store and render with unprecedented performance extremely detailed 3D mesh models on WebGL-enabled browsers, as well as on hardware-constrained mobile devices.

Animation on the web

“…Similarly, Normal maps and displacement maps give the illusion of more structured details. For 3D data transmission, textures can be used by using the quantization threshold [Rodríguez et al 2013;Maglo et al 2012;Schwartz et al 2011] which represents a factor by which to quantize the geometry, which can be transmitted separately in a JSON file. Once the client side application knows of the factor, the decompression of the PNG takes place to retrieve the chunk of relevant data into a WebGL texture.…”

Big Data Geospatial Processing for Massive Aerial LiDAR Datasets

“…Nowadays, LiDAR technology stands out as one of the most valuable sources of geospatial information, providing enormous benefits in a wide range of scientific and professional fields, such as agroforestry [1] or land monitoring [2], among many others. Nevertheless, LiDAR is also considered as a challenge when it comes to developing efficient proposals to handle the extremely large volumes of data this surveying method is able to collect; hence, researchers are forced to constantly look for new approaches and solutions in order to overcome all constraints related to the manipulation of such volumes of information [3,4].…”