@inproceedings{5e80d005d1b24dab9e3c61449c7b4a0b,
title = "Cinema Darkroom: A Deferred Rendering Framework for Large-Scale Datasets",
abstract = "This paper presents a framework that fully leverages the advantages of a deferred rendering approach for the interactive visualization of large-scale datasets. Geometry buffers (G-Buffers) are generated and stored in situ, and shading is performed post hoc in an interactive image-based rendering front end. This decoupled framework has two major advantages. First, the G-Buffers only need to be computed and stored once-which corresponds to the most expensive part of the rendering pipeline. Second, the stored G-Buffers can later be consumed in an image-based rendering front end that enables users to interactively adjust various visualization parameters-such as the applied color map or the strength of ambient occlusion-where suitable choices are often not known a priori. This paper demonstrates the use of Cinema Darkroom on several real-world datasets, highlighting CD's ability to effectively decouple the complexity and size of the dataset from its visualization.",
keywords = "Deferred Rendering, Image Databases, Image-Based Shading, In Situ Visualization, Post Hoc Analysis",
author = "Jonas Lukasczyk and Christoph Garth and Matthew Larsen and Wito Engelke and Ingrid Hotz and David Rogers and James Ahrens and Ross MacIejewski",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 10th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2020 ; Conference date: 25-10-2020",
year = "2020",
month = oct,
doi = "10.1109/LDAV51489.2020.00011",
language = "English (US)",
series = "Proceedings - 2020 IEEE 10th Symposium on Large Data Analysis and Visualization, LDAV 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "37--41",
booktitle = "Proceedings - 2020 IEEE 10th Symposium on Large Data Analysis and Visualization, LDAV 2020",
}