468 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
Adversarial Sparse-View CBCT Artifact Reduction
We present an effective post-processing method to reduce the artifacts from
sparsely reconstructed cone-beam CT (CBCT) images. The proposed method is based
on the state-of-the-art, image-to-image generative models with a perceptual
loss as regulation. Unlike the traditional CT artifact-reduction approaches,
our method is trained in an adversarial fashion that yields more perceptually
realistic outputs while preserving the anatomical structures. To address the
streak artifacts that are inherently local and appear across various scales, we
further propose a novel discriminator architecture based on feature pyramid
networks and a differentially modulated focus map to induce the adversarial
training. Our experimental results show that the proposed method can greatly
correct the cone-beam artifacts from clinical CBCT images reconstructed using
1/3 projections, and outperforms strong baseline methods both quantitatively
and qualitatively
Advances in the application of deep learning methods to digital rock technology
Digital rock technology is becoming essential in reservoir engineering and petrophysics. Three-dimensional digital rock reconstruction, image resolution enhancement, image segmentation, and rock parameters prediction are all crucial steps in enabling the overall analysis of digital rocks to overcome the shortcomings and limitations of traditional methods. Artificial intelligence technology, which has started to play a significant role in many different fields, may provide a new direction for the development of digital rock technology. This work presents a systematic review of the deep learning methods that are being applied to tasks within digital rock analysis, including the reconstruction of digital rocks, high-resolution image acquisition, grayscale image segmentation, and parameter prediction. The results of these applications prove that state-of-the-art deep learning methods can help advance and provide a new approach to scientific knowledge in the field of digital rocks. This work also discusses future research and developments on the application of deep learning methods to digital rock technology.Cited as:Â Li, X., Li, B., Liu, F., Li, T., Nie, X. Advances in the application of deep learning methods to digital rock technology. Advances in Geo-Energy Research, 2023, 8(1): 5-18. https://doi.org/10.46690/ager.2023.04.0
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