Crédits: 19 juil. 2013
Link to project page & press release: http://www.disneyresearch.com/project...
This
paper describes a method for scene reconstruction of complex, detailed
environments from 3D light fields. Densely sampled light fields in the
order of 10^9 light rays allow us to capture the real world in
unparalleled detail, but efficiently processing this amount of data to
generate an equally detailed reconstruction represents a significant
challenge to existing algorithms. We propose an algorithm that leverages
coherence in massive light fields by breaking with a number of
established practices in image-based reconstruction. Our algorithm first
computes reliable depth estimates specifically around object boundaries
instead of interior regions, by operating on individual light rays
instead of image patches. More homogeneous interior regions are then
processed in a fine-to-coarse procedure rather than the standard
coarse-to-fine approaches. At no point in our method is any form of
global optimization performed. This allows our algorithm to retain
precise object contours while still ensuring smooth reconstructions in
less detailed areas. While the core reconstruction method handles
general unstructured input, we also introduce a sparse representation
and a propagation scheme for reliable depth estimates which make our
algorithm particularly effective for 3D input, enabling fast and memory
efficient processing of "Gigaray light fields" on a standard GPU. We
show dense 3D reconstructions of highly detailed scenes, enabling
applications such as automatic segmentation and image-based rendering,
and provide an extensive evaluation and comparison to existing
image-based reconstruction techniques.