3 research outputs found
Augmented Reality Using Full Panoramic Captured Scene Light-Depth Maps 134
Ask for a programmer and an artist what is the Holy Grail of computer graphics and you may hear completely different awnsers. It’s part of the competence of computers to strive for reproduce reality, to mimic it bit by bit, lumen by lumen. And it’s in the core o
Automatic Scene Inference for 3D Object Compositing
We present a user-friendly image editing system that supports a drag-and-drop
object insertion (where the user merely drags objects into the image, and the
system automatically places them in 3D and relights them appropriately),
post-process illumination editing, and depth-of-field manipulation. Underlying
our system is a fully automatic technique for recovering a comprehensive 3D
scene model (geometry, illumination, diffuse albedo and camera parameters) from
a single, low dynamic range photograph. This is made possible by two novel
contributions: an illumination inference algorithm that recovers a full
lighting model of the scene (including light sources that are not directly
visible in the photograph), and a depth estimation algorithm that combines
data-driven depth transfer with geometric reasoning about the scene layout. A
user study shows that our system produces perceptually convincing results, and
achieves the same level of realism as techniques that require significant user
interaction
Inverse Rendering Techniques for Physically Grounded Image Editing
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.Comment: PhD thesis, Computer Science, University of Illinois at
Urbana-Champaign, 201