Date & Time : September 10 04:15 pm - 06:30 pm Location : HS1 Chair : Wolfgang Broll, TU Ilmenau Papers :
Interactive Near-Field Illumination for Photorealistic Augmented Reality on Mobile Devices
Authors: Kai Rohmer, Wolfgang Büschel, Raimund Dachselt, Thorsten Grosch
Abstract : Mobile devices become more and more important today, especially for augmented
reality (AR) applications in which the camera of the mobile device acts like
a window into the mixed reality world. Up to now, no photorealistic
augmentation is possible since the computational power of the mobile devices
is still too weak. Even a streaming solution from a stationary PC would cause
a latency, that affects user interactions considerably. Therefore, we
introduce a differential illumination method that allows for a consistent
illumination of the inserted virtual objects on mobile devices, avoiding a
delay. The necessary computation effort is shared between a stationary PC and
the mobile devices to make use of the capacities available on both sides. The
method is designed such that only a minimum amount of data has to be
transferred asynchronously between the stationary PC and one or multiple
mobile devices. This allows for an interactive illumination of virtual
objects with a consistent appearance under both temporally and spatially
varying real illumination conditions. To describe the complex near-field
illumination in an indoor scenario, multiple HDR video cameras are used to
capture the illumination from multiple directions. In this way, sources of
illumination can be considered that are not directly visible to the mobile
device because of occlusions and the limited field of view of built-in
cameras.
Delta Voxel Cone Tracing
Author: Tobias Alexander Franke
Abstract : Mixed reality applications which must provide visual coherence between
synthetic and real objects need relighting solutions for both: synthetic
objects have to match lighting conditions of their real counterparts, while
real surfaces need to account for the change in illumination introduced by
the presence of an additional synthetic object. In this paper we present a
novel relighting solution called Delta Voxel Cone Tracing to compute both
direct shadows and first bounce mutual indirect illumination. We introduce a
voxelized, pre-filtered representation of the combined real and synthetic
surfaces together with the extracted illumination difference due to the
augmentation. In a final gathering step this representation is cone-traced
and superimposed onto both types of surfaces, adding additional light from
indirect bounces and synthetic shadows from antiradiance present in the
volume. The algorithm computes results at interactive rates, is temporally
coherent and to our knowledge provides the first real-time rasterizer
solution for mutual diffuse, glossy and perfect specular indirect reflections
between synthetic and real surfaces in mixed reality.
Importance Weighted Image Enhancement for Prosthetic Vision: An Augmentation Framework
Authors: Chris McCarthy, Nick Barnes
Abstract : Augmentations to enhance perception in prosthetic vision (also known as
bionic eyes) have the potential to improve functional outcomes significantly
for implantees. In current (and near-term) implantable electrode arrays
resolution and dynamic range are highly constrained in comparison to images
from modern cameras that can be head mounted. In this paper, we propose a
novel, generally applicable adaptive contrast augmentation framework for
prosthetic vision that addresses the specific perceptual needs of low
resolution and low dynamic range displays. The scheme accepts an externally
defined pixel-wise weighting of importance describing features of the image
to enhance in the output dynamic range. Our approach explicitly incorporates
the logarithmic scaling of enhancement required in human visual perception to
ensure perceivability of all contrast augmentations. It requires no
pre-existing contrast, and thus extends previous work in local contrast
enhancement to a formulation for general image augmentation. We demonstrate
the generality of our augmentation scheme for scene structure and looming
object enhancement using simulated prosthetic vision.
P-HRTF: Efficient Personalized HRTF Computation for High-Fidelity Spatial Sound
Abstract : Accurate rendering of 3D spatial audio for interactive virtual auditory
displays requires the use of personalized head-related transfer functions
(HRTFs). We present a new approach to compute personalized HRTFs for any
individual using a method that combines state-of-the-art image-based 3D
modeling with an efficient numerical simulation pipeline. Our 3D modeling
framework enables capture of the listener's head and torso using
consumer-grade digital cameras to estimate a high-resolution non-parametric
surface representation of the head, including the extended vicinity of the
listener's ear. We leverage sparse structure from motion and dense surface
reconstruction techniques to generate a 3D mesh. This mesh is used as input
to a numeric sound propagation solver, which uses acoustic reciprocity and
Kirchhoff surface integral representation to efficiently compute an
individual's personalized HRTF. The overall computation takes tens of minutes
on multi-core desktop machine. We have used our approach to compute the
personalized HRTFs of few individuals, and we present our preliminary
evaluation here. To the best of our knowledge, this is the first commodity
technique that can be used to compute personalized HRTFs in a lab or home
setting.
Visibility-Based Blending for Real-Time Applications
Abstract : There are many situations in which virtual objects are presented
half-transparently on a background in real time applications. In such cases,
we often want to show the object with constant visibility. However, using the
conventional alpha blending, visibility of a blended object substantially
varies depending on colors, textures, and structures of the background scene.
To overcome this problem, we present a framework for blending images based on
a subjective metric of visibility. In our method, a blending parameter is
locally and adaptively optimized so that visibility of each location achieves
the targeted level. To predict visibility of an object blended by an
arbitrary parameter, we utilize one of the error visibility metrics that have
been developed for image quality assessment. In this study, we demonstrated
that the metric we used can linearly predict visibility of a blended pattern
on various texture images, and showed that the proposed blending methods can
work in practical situations assuming augmented reality.