Point- and volume-based neural rendering
Neural rendering has recently emerged as a powerful paradigm for creating novel views from a set of input images. Broadly speaking, neural rendering methods rely on a set of photographs for learning a representation of the underlying scene. The learned representation can subsequently be queried to generate images of that same scene from a viewpoint that was not previously captured. In this internship, we will explore novel methods for combining existing neural rendering techniques, namely: point- and volume-based. These two techniques are complementary to each other: volume-based methods are flexible and can learn complex geometry, while point-based techniques are fast. If successful, this research will lead to advances which will likely have impact in virtual and augmented reality, historical preservation, and entertainment.
Voir la description complète du projetJean-Francois Lalonde
Inria Sophia Antipolis - Méditerranée Research Centre
Computer science
Education
Université Laval
Globalink Research Award
