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Chair of Visual Computing
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  1. Friedrich-Alexander-Universität
  2. Technische Fakultät
  3. Department Informatik

Chair of Visual Computing

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  • Research
    • Rendering and Visualization
    • Geometric Modeling and 3D Reconstruction
    • Virtual, Mixed, and Augmented Reality
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  3. Publications 2021
  4. Spatio-temporal filtered motion DAGs for path-tracing

Spatio-temporal filtered motion DAGs for path-tracing

In page navigation: Publications
  • Publications 2020
  • Publications 2021
    • ADOP: Approximate Differentiable One-Pixel Point Rendering
    • Efficient Unbiased Volume Path Tracing on the GPU
    • Interactive Path Tracing and Reconstruction of Sparse Volumes
    • Projection Mapping for In-Situ Surgery Planning by the Example of DIEP Flap Breast Reconstruction
    • Robust marker-based projector-camera synchronization
    • Scan&Paint: Image-based Projection Painting
    • Spatio-temporal filtered motion DAGs for path-tracing
  • Publications 2022

Spatio-temporal filtered motion DAGs for path-tracing

Magdalena Martinek

Dipl.-Inf. Magdalena Martinek

Department of Computer Science
Chair of Computer Science 9 (Computer Graphics)

Cauerstraße 11
91058 Erlangen
  • Email: magdalena.martinek@fau.de
  • Website: http://www9.informatik.uni-erlangen.de/people/card/magdalena/prus/

Paper

  • Martinek M., Thiemann P., Stamminger M.:
    Spatio-temporal filtered motion DAGs for path-tracing
    In: Computers & Graphics 99 (2021), p. 224-233
    ISSN: 0097-8493
    DOI: 10.1016/j.cag.2021.07.008
    URL: https://www.lgdv.tf.fau.de/?p=2268
    BibTeX: Download

Abstract

Motion Blur is an important effect of photo-realistic rendering. Distribution ray tracing can simulate motion blur very well by integrating light, both over the spatial and the temporal domain. However, increasing the problem by the temporal dimension entails many challenges, particularly in cinematic multi-bounce path tracing of complex scenes, where heavy-weight geometry with complex lighting and even offscreen elements contribute to the final image. In this paper, we propose the Motion DAG (Directed Acyclic Graph), a novel data structure that filters an entire animation sequence of an object, both in the spatial and temporal domain. These Motion DAGs interleave a temporal interval binary tree for filtering time consecutive data and a sparse voxel octree (SVO), which simplifies spatially nearby data. Motion DAGs are generated in a pre-process and can be easily integrated in a conventional physically based path tracer. Our technique is designed to target motion blur of small objects, where coarse representations are sufficient. Specifically, in this scenario our results show that it is possible to significantly reduce both, memory consumption and render time.

Chair of Visual Computing
(Lehrstuhl für Graphische Datenverarbeitung)

Cauerstraße 11
91058 Erlangen
Deutschland
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