Hasso-Plattner-Institut
Prof. Dr. Jürgen Döllner
  
 

01.09.2020

Papers accepted for Web3D 2020

Two papers were accepted for publication at the International Conference on Web3D Technology (ACM Web3D 2020).

  • Philipp Otto, Daniel Limberger, and Jürgen Döllner: "Physically-based Environment and Area Lighting using Progressive Rendering in WebGL"Clusters in Massive Data Using 3D Scatter Plots and WebGL"
  • Lukas Wagner, Willy Scheibel, Daniel Limberger, Matthias Trapp, and Jürgen Döllner: "A Framework for Interactive Exploration of

The conference is held as a virtual conference from November 9th until November 13th. More information at web3d.siggraph.org/.

Abstracts

Physically-based Environment and Area Lighting using Progressive Rendering in WebGL

This paper presents a progressive rendering approach that enables rendering of static 3D scenes, lit by physically-based environment and area lights. Multi-frame sampling strategies are used to approximate elaborate lighting that is refined while showing intermediate results to the user. The presented approach enables interactive yet high-quality rendering in the web and runs on a wide range of devices including low-performance hardware such as mobile devices. An open-source implementation of the described techniques using TypeScript and WebGL 2.0 is presented and provided. For evaluation, we compare our rendering results to both a path tracer and a physically-based rasterizer. Our findings show that the approach approximates the lighting and shadowing of the path-traced reference well while being faster than the compared rasterizer.

A Framework for Interactive Exploration of Clusters in Massive Data Using 3D Scatter Plots and WebGL

This paper presents a rendering framework for the visualization of massive point datasets in the web. It includes highly interactive point rendering, cluster visualization, basic interaction methods, and importance-based labeling, while being available for both mobile and desktop browsers. The rendering style is customizable, as shown in figure 1. Our evaluation indicates that the framework facilitates interactive visualization of tens of millions of raw data points even without dynamic filtering or aggregation.