51,462 research outputs found
Direct Iterative Reconstruction of Multiple Basis Material Images in Photon-counting Spectral CT
In this work, we perform direct material reconstruction from spectral CT data
using a model based iterative reconstruction (MBIR) approach. Material
concentrations are measured in volume fractions, whose total is constrained by
a maximum of unity. A phantom containing a combination of 4 basis materials
(water, iodine, gadolinium, calcium) was scanned using a photon-counting
detector. Iodine and gadolinium were chosen because of their common use as
contrast agents in CT imaging. Scan data was binned into 5 energy (keV) levels.
Each energy bin in a calibration scan was reconstructed, allowing the linear
attenuation coefficient of each material for every energy to be estimated by a
least-squares fit to ground truth in the image domain. The resulting matrix, for energies and materials, is incorporated into the forward
model in direct reconstruction of the basis material images with spatial
and/or inter-material regularization. In reconstruction from a subsequent
low-concentration scan, volume fractions within regions of interest (ROIs) are
found to be close to the ground truth. This work is meant to lay the foundation
for further work with phantoms including spatially coincident mixtures of
contrast materials and/or contrast agents in widely varying concentrations,
molecular imaging from animal scans, and eventually clinical applications
Extracting Triangular 3D Models, Materials, and Lighting From Images
We present an efficient method for joint optimization of topology, materials
and lighting from multi-view image observations. Unlike recent multi-view
reconstruction approaches, which typically produce entangled 3D representations
encoded in neural networks, we output triangle meshes with spatially-varying
materials and environment lighting that can be deployed in any traditional
graphics engine unmodified. We leverage recent work in differentiable
rendering, coordinate-based networks to compactly represent volumetric
texturing, alongside differentiable marching tetrahedrons to enable
gradient-based optimization directly on the surface mesh. Finally, we introduce
a differentiable formulation of the split sum approximation of environment
lighting to efficiently recover all-frequency lighting. Experiments show our
extracted models used in advanced scene editing, material decomposition, and
high quality view interpolation, all running at interactive rates in
triangle-based renderers (rasterizers and path tracers). Project website:
https://nvlabs.github.io/nvdiffrec/ .Comment: Project website: https://nvlabs.github.io/nvdiffrec
Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture
This paper addresses the problem of interpolating visual textures. We
formulate this problem by requiring (1) by-example controllability and (2)
realistic and smooth interpolation among an arbitrary number of texture
samples. To solve it we propose a neural network trained simultaneously on a
reconstruction task and a generation task, which can project texture examples
onto a latent space where they can be linearly interpolated and projected back
onto the image domain, thus ensuring both intuitive control and realistic
results. We show our method outperforms a number of baselines according to a
comprehensive suite of metrics as well as a user study. We further show several
applications based on our technique, which include texture brush, texture
dissolve, and animal hybridization.Comment: Accepted to CVPR'1
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