Towards Predictive Rendering in Virtual Reality

Dissertation, Universität Bonn, Jan. 2008
 

Abstract

The strive for generating predictive images, i.e., images representing radiometrically correct renditions of reality, has been a longstanding problem in computer graphics. The exactness of such images is extremely important for Virtual Reality applications like Virtual Prototyping, where users need to make decisions impacting large investments based on the simulated images.

Unfortunately, generation of predictive imagery is still an unsolved problem due to manifold reasons, especially if real-time restriction apply. First, existing scenes used for rendering are not modeled accurately enough to create predictive images. Second, even with huge computational efforts existing rendering algorithms are not able to produce radiometrically correct images. Third, current display devices need to convert rendered images into some low-dimensional color space, which prohibits display of radiometrically correct images.

Overcoming these limitations is the focus of current state-of-the-art research. This thesis also contributes to this task. First, it briefly introduces the necessary background and identifies the steps required for real-time predictive image generation. Then, existing techniques targeting these steps are presented and their limitations are pointed out. To solve some of the remaining problems, novel techniques are proposed. They cover various steps in the predictive image generation process, ranging from accurate scene modeling over efficient data representation to high-quality, real-time rendering.

A special focus of this thesis lays on real-time generation of predictive images using bidirectional texture functions (BTFs), i.e., very accurate representations for spatially varying surface materials. The techniques proposed by this thesis enable efficient handling of BTFs by compressing the huge amount of data contained in this material representation, applying them to geometric surfaces using texture and BTF synthesis techniques, and rendering BTF covered objects in real-time. Further approaches proposed in this thesis target inclusion of real-time global illumination effects or more efficient rendering using novel level-of-detail representations for geometric objects. Finally, this thesis assesses the rendering quality achievable with BTF materials, indicating a significant increase in realism but also confirming the remainder of problems to be solved to achieve truly predictive image generation.

(Thesis submission date: Oct. 2006)

Download: http://nbn-resolving.de/urn:nbn:de:hbz:5N-13143

Overview and additional material: http://cg.cs.uni-bonn.de/personal-pages/meseth/

Bibtex

@PHDTHESIS{meseth-2006-phd,
    author = {Meseth, Jan},
     title = {Towards Predictive Rendering in Virtual Reality},
      type = {Dissertation},
      year = {2008},
     month = jan,
    school = {Universit{\"a}t Bonn},
  abstract = {The strive for generating predictive images, i.e., images representing radiometrically correct
              renditions of reality, has been a longstanding problem in computer graphics. The exactness of such
              images is extremely important for Virtual Reality applications like Virtual Prototyping, where users
              need to make decisions impacting large investments based on the simulated images.
              
              Unfortunately, generation of predictive imagery is still an unsolved problem due to manifold
              reasons, especially if real-time restriction apply. First, existing scenes used for rendering are
              not modeled accurately enough to create predictive images. Second, even with huge computational
              efforts existing rendering algorithms are not able to produce radiometrically correct images. Third,
              current display devices need to convert rendered images into some low-dimensional color space, which
              prohibits display of radiometrically correct images.
              
              Overcoming these limitations is the focus of current state-of-the-art research. This thesis also
              contributes to this task. First, it briefly introduces the necessary background and identifies the
              steps required for real-time predictive image generation. Then, existing techniques targeting these
              steps are presented and their limitations are pointed out. To solve some of the remaining problems,
              novel techniques are proposed. They cover various steps in the predictive image generation process,
              ranging from accurate scene modeling over efficient data representation to high-quality, real-time
              rendering.
              
              A special focus of this thesis lays on real-time generation of predictive images using bidirectional
              texture functions (BTFs), i.e., very accurate representations for spatially varying surface
              materials. The techniques proposed by this thesis enable efficient handling of BTFs by compressing
              the huge amount of data contained in this material representation, applying them to geometric
              surfaces using texture and BTF synthesis techniques, and rendering BTF covered objects in real-time.
              Further approaches proposed in this thesis target inclusion of real-time global illumination effects
              or more efficient rendering using novel level-of-detail representations for geometric objects.
              Finally, this thesis assesses the rendering quality achievable with BTF materials, indicating a
              significant increase in realism but also confirming the remainder of problems to be solved to
              achieve truly predictive image generation.},
       url = {http://nbn-resolving.de/urn:nbn:de:hbz:5N-13143}
}