Three-dimensional (3D) graphics are commonplace in many applications such as digital
entertainment, cultural heritage, architecture and scientific simulation. These data are increasingly rich
and detailed; as a complex 3D scene may contain millions of geometric primitives, enriched with various
appearance attributes such as texture maps designed to produce a realistic material appearance, as well
as animation data.
The way of consuming and visualizing this 3D content is now evolving from standard screens to Virtual
and Mixed Reality (VR/MR). However, the visualization and interaction with 6 degrees of freedom with
large and complex 3D scene remains an unresolved issue in such immersive environments, especially
when the scene is stored on a remote server. Two distinct bottlenecks exist: (1) the potential complexity
of a 3D scene that can be displayed to the user on a VR/MR head-mounted display is substantially
smaller than for a standard screen, because the GPU must generate 4 times more images (to ensure two
images per frame and a sufficient frame-rate to prevent motion sickness); (2) since an increasing
number of VR/MR applications consider 3D data stored on remote servers, strong latency problems may
be encountered, caused by the streaming of the scene on the display device.
The proposed PhD position is funded by the ANR PISCo project (Perceptual Levels of Detail for
Interactive and Immersive Remote Visualization of Complex 3D Scenes) which aims at proposing novel
algorithms and tools allowing interactive visualization, in these constrained contexts (Virtual and Mixed
reality, with local/remote 3D content), with a very high quality of user experience. As 3D scenes are
visualized through a certain viewport, we seek to optimize the display in this viewport by proposing (1)
Tools for the generation and compression of high quality levels of details, (2) Visual quality metrics
capable of predicting the quality of these levels of detail and driving their generation, (3) Visual
attention models capable of predicting where the observer is looking and thus selecting and filtering the
primitives and levels of detail. A distinctive property of the project lies into the fact that we will consider
rich 3D data, including not only geometric information but also animation and complex physically based
materials represented by texture maps (color, metalness, roughness, normals).
The ANR PISCo project is funded by the French Research Agency and involves three academic partners:
LIRIS (University of Lyon), LS2N (University of Nantes) and INRIA TITANE (Sophia-Antipolis).
University of Lyon
: Master’s or Diploma degree in Computer Science, strong experience with C++
programming, good knowledge of image processing and computer graphics.
Duration : 3 years.