AI/ML

Inverse Rendering Techniques for Physically Grounded Image Editing. (arXiv:2001.00986v1 [cs.CV])



From a single picture of a scene, people can typically grasp the spatial
layout immediately and even make good guesses at materials properties and where
light is coming from to illuminate the scene. For example, we can reliably tell
which objects occlude others, what an object is made of and its rough shape,
regions that are illuminated or in shadow, and so on. It is interesting how
little is known about our ability to make these determinations; as such, we are
still not able to robustly “teach” computers to make the same high-level
observations as people. This document presents algorithms for understanding
intrinsic scene properties from single images. The goal of these inverse
rendering techniques is to estimate the configurations of scene elements
(geometry, materials, luminaires, camera parameters, etc) using only
information visible in an image. Such algorithms have applications in robotics
and computer graphics. One such application is in physically grounded image
editing: photo editing made easier by leveraging knowledge of the physical
space. These applications allow sophisticated editing operations to be
performed in a matter of seconds, enabling seamless addition, removal, or
relocation of objects in images.

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