3,444 research outputs found
Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
3D Morphable Model (3DMM) based methods have achieved great success in
recovering 3D face shapes from single-view images. However, the facial textures
recovered by such methods lack the fidelity as exhibited in the input images.
Recent work demonstrates high-quality facial texture recovering with generative
networks trained from a large-scale database of high-resolution UV maps of face
textures, which is hard to prepare and not publicly available. In this paper,
we introduce a method to reconstruct 3D facial shapes with high-fidelity
textures from single-view images in-the-wild, without the need to capture a
large-scale face texture database. The main idea is to refine the initial
texture generated by a 3DMM based method with facial details from the input
image. To this end, we propose to use graph convolutional networks to
reconstruct the detailed colors for the mesh vertices instead of reconstructing
the UV map. Experiments show that our method can generate high-quality results
and outperforms state-of-the-art methods in both qualitative and quantitative
comparisons.Comment: Accepted to CVPR 2020. The source code is available at
https://github.com/FuxiCV/3D-Face-GCN
Shape Animation with Combined Captured and Simulated Dynamics
We present a novel volumetric animation generation framework to create new
types of animations from raw 3D surface or point cloud sequence of captured
real performances. The framework considers as input time incoherent 3D
observations of a moving shape, and is thus particularly suitable for the
output of performance capture platforms. In our system, a suitable virtual
representation of the actor is built from real captures that allows seamless
combination and simulation with virtual external forces and objects, in which
the original captured actor can be reshaped, disassembled or reassembled from
user-specified virtual physics. Instead of using the dominant surface-based
geometric representation of the capture, which is less suitable for volumetric
effects, our pipeline exploits Centroidal Voronoi tessellation decompositions
as unified volumetric representation of the real captured actor, which we show
can be used seamlessly as a building block for all processing stages, from
capture and tracking to virtual physic simulation. The representation makes no
human specific assumption and can be used to capture and re-simulate the actor
with props or other moving scenery elements. We demonstrate the potential of
this pipeline for virtual reanimation of a real captured event with various
unprecedented volumetric visual effects, such as volumetric distortion,
erosion, morphing, gravity pull, or collisions
Interactive global illumination on the CPU
Computing realistic physically-based global illumination in real-time remains one
of the major goals in the fields of rendering and visualisation; one that has not
yet been achieved due to its inherent computational complexity. This thesis focuses
on CPU-based interactive global illumination approaches with an aim to
develop generalisable hardware-agnostic algorithms. Interactive ray tracing is reliant
on spatial and cache coherency to achieve interactive rates which conflicts
with needs of global illumination solutions which require a large number of incoherent
secondary rays to be computed. Methods that reduce the total number of
rays that need to be processed, such as Selective rendering, were investigated to
determine how best they can be utilised.
The impact that selective rendering has on interactive ray tracing was analysed
and quantified and two novel global illumination algorithms were developed,
with the structured methodology used presented as a framework. Adaptive Inter-
leaved Sampling, is a generalisable approach that combines interleaved sampling
with an adaptive approach, which uses efficient component-specific adaptive guidance
methods to drive the computation. Results of up to 11 frames per second
were demonstrated for multiple components including participating media. Temporal Instant Caching, is a caching scheme for accelerating the computation of
diffuse interreflections to interactive rates. This approach achieved frame rates
exceeding 9 frames per second for the majority of scenes. Validation of the results
for both approaches showed little perceptual difference when comparing
against a gold-standard path-traced image. Further research into caching led to
the development of a new wait-free data access control mechanism for sharing the
irradiance cache among multiple rendering threads on a shared memory parallel
system. By not serialising accesses to the shared data structure the irradiance
values were shared among all the threads without any overhead or contention,
when reading and writing simultaneously. This new approach achieved efficiencies
between 77% and 92% for 8 threads when calculating static images and animations.
This work demonstrates that, due to the
flexibility of the CPU, CPU-based
algorithms remain a valid and competitive choice for achieving global illumination
interactively, and an alternative to the generally brute-force GPU-centric
algorithms
Interactive high fidelity visualization of complex materials on the GPU
Documento submetido para revisão pelos pares. A publicar em Computers & Graphics. ISSN 0097-8493. 37:7 (nov. 2013) p. 809–819High fidelity interactive rendering is of major importance for footwear designers, since it allows experimenting with virtual prototypes of new products, rather than producing expensive physical mock-ups. This requires capturing the appearance of complex materials by resorting to image based approaches, such as the Bidirectional Texture Function (BTF), to allow subsequent interactive visualization, while still maintaining the capability to edit the materials' appearance. However, interactive global illumination rendering of compressed editable BTFs with ordinary computing resources remains to be demonstrated.
In this paper we demonstrate interactive global illumination by using a GPU ray tracing engine and the Sparse Parametric Mixture Model representation of BTFs, which is particularly well suited for BTF editing. We propose a rendering pipeline and data layout which allow for interactive frame rates and provide a scalability analysis with respect to the scene's complexity. We also include soft shadows from area light sources and approximate global illumination with ambient occlusion by resorting to progressive refinement, which quickly converges to an high quality image while maintaining interactive frame rates by limiting the number of rays shot per frame. Acceptable performance is also demonstrated under dynamic settings, including camera movements, changing lighting conditions and dynamic geometry.Work partially funded by QREN project nbr. 13114 TOPICShoe and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projectPEst-OE/EEI/UI0752/2011
Normative Data and Minimally Detectable Change for Inner Retinal Layer Thicknesses Using a Semi-automated OCT Image Segmentation Pipeline
Neurodegenerative and neuroinflammatory diseases regularly cause optic nerve and
retinal damage. Evaluating retinal changes using optical coherence tomography (OCT)
in diseases like multiple sclerosis has thus become increasingly relevant. However,
intraretinal segmentation, a necessary step for interpreting retinal changes in the context
of these diseases, is not standardized and often requires manual correction. Here
we present a semi-automatic intraretinal layer segmentation pipeline and establish
normative values for retinal layer thicknesses at the macula, including dependencies on
age, sex, and refractive error. Spectral domain OCT macular 3D volume scans were
obtained from healthy participants using a Heidelberg Engineering Spectralis OCT. A
semi-automated segmentation tool (SAMIRIX) based on an interchangeable third-party
segmentation algorithm was developed and employed for segmentation, correction, and
thickness computation of intraretinal layers. Normative data is reported froma 6mmEarly
Treatment Diabetic Retinopathy Study (ETDRS) circle around the fovea. An interactive
toolbox for the normative database allows surveying for additional normative data. We
cross-sectionally evaluated data from218 healthy volunteers (144 females/74males, age
36.5 ± 12.3 years, range 18–69 years). Average macular thickness (MT) was 313.70 ±
12.02 μm, macular retinal nerve fiber layer thickness (mRNFL) 39.53 ± 3.57 μm, ganglion
cell and inner plexiform layer thickness (GCIPL) 70.81 ± 4.87 μm, and inner nuclear layer
thickness (INL) 35.93 ± 2.34 μm. All retinal layer thicknesses decreased with age. MT
and GCIPL were associated with sex, with males showing higher thicknesses. Layer
thicknesses were also positively associated with each other. Repeated-measurement
reliability for the manual correction of automatic intraretinal segmentation results was excellent, with an intra-class correlation coefficient >0.99 for all layers. The SAMIRIX
toolbox can simplify intraretinal segmentation in research applications, and the normative
data application may serve as an expandable reference for studies, in which normative
data cannot be otherwise obtained
Light field image processing: an overview
Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data
Fast unsupervised multiresolution color image segmentation using adaptive gradient thresholding and progressive region growing
In this thesis, we propose a fast unsupervised multiresolution color image segmentation algorithm which takes advantage of gradient information in an adaptive and progressive framework. This gradient-based segmentation method is initialized by a vector gradient calculation on the full resolution input image in the CIE L*a*b* color space. The resultant edge map is used to adaptively generate thresholds for classifying regions of varying gradient densities at different levels of the input image pyramid, obtained through a dyadic wavelet decomposition scheme. At each level, the classification obtained by a progressively thresholded growth procedure is combined with an entropy-based texture model in a statistical merging procedure to obtain an interim segmentation. Utilizing an association of a gradient quantized confidence map and non-linear spatial filtering techniques, regions of high confidence are passed from one level to another until the full resolution segmentation is achieved. Evaluation of our results on several hundred images using the Normalized Probabilistic Rand (NPR) Index shows that our algorithm outperforms state-of the art segmentation techniques and is much more computationally efficient than its single scale counterpart, with comparable segmentation quality
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Visualization-driven Structural and Statistical Analysis of Turbulent Flows
Knowledge extraction from data volumes of ever increasing size requires ever more flexible tools to facilitate interactive query. In- teractivity enables real-time hypothesis testing and scientific discovery, but can generally not be achieved without some level of data reduction. The approach described in this paper combines multi-resolution access, region-of-interest extraction, and structure identification in order to pro- vide interactive spatial and statistical analysis of a terascale data volume. Unique aspects of our approach include the incorporation of both local and global statistics of the flow structures, and iterative refinement fa- cilities, which combine geometry, topology, and statistics to allow the user to effectively tailor the analysis and visualization to the science. Working together, these facilities allow a user to focus the spatial scale and domain of the analysis and perform an appropriately tailored mul- tivariate visualization of the corresponding data. All of these ideas and algorithms are instantiated in a deployed visualization and analysis tool called VAPOR, which is in routine use by scientists internationally. In data from a 10243 simulation of a forced turbulent flow, VAPOR allowed us to perform a visual data exploration of the flow properties at interac- tive speeds, leading to the discovery of novel scientific properties of the flow, in the form of two distinct vortical structure populations. These structures would have been very difficult (if not impossible) to find with statistical overviews or other existing visualization-driven analysis ap- proaches. This kind of intelligent, focused analysis/refinement approach will become even more important as computational science moves to- wards petascale applications
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