Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019)

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

List of projects for 3d reconstruction

Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction: Implementation & Demo

Data and code for Shen, Horikawa, Majima, and Kamitani (2019) Deep image reconstruction from human brain activity. PLoS Comput. Biol. http://dx.doi.org/10.1371/journal.pcbi.1006633.

Official PyTorch implementation of "Single Image HDR Reconstruction Using a CNN with Masked Features and Perceptual Loss" (SIGGRAPH 2020)

A deep look at some recon methodologies and web-application vulnerabilities of my interest where I will merge all my notes gathered from books, videos, articles and own experience with bug bounty hunting / web and network hacking

Pytorch version of Deep3DFaceReconstruction

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set

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The study of hydrodynamic behavior and water-rock interaction mechanisms is typically characterized by high computational efficiency requirements, to allow for the fast and accurate extraction of structural information. Therefore, we chose to use deep learning models to achieve these requirements. In this paper we started by comparing the image segmentation performance of a series of autoencoder architectures on complex geometries of porous media. The goal was to extract hydrodynamic connectivity channels and the mineral composition of rock samples on SEM (Scanning electron microscopy) data, obtained with a 0.97 accuracy. We then focused on improving the computational efficiency of LBM by using GPU acceleration, which allowed us to rapidly simulate structural flow field features of complex porous media. The results obtained showed that we were able to improve the computational efficiency by a factor of 21 in our device environment. We subsequently employed a SWD-Cycle-GAN technique to migrate sedimentation features to the initial 2D structure slices to reconstruct a 3D (three-dimensional) porous media geometry, that fits the depositional features more closely. Overall, we propose a new method for 3D structure reconstruction and permeability performance analysis of porous media, based on deep learning. The proposed method is fast, efficient and accurate.

The network inputs a 2D X-ray Image from 1/2/3 different views and outputs a 3D CT Volume.

Python framework for designing and training Deep Learning models to reconstruct 12-lead Electrocardiograms from 3-lead inputs and classify clinical conditions effectively.

Use Convolutional Recurrent Neural Network to recognize the Handwritten Word text image without pre segmentation into words or characters. Use CTC loss Function to train.

This repository seeks to investigate the usage of Cascades of Convolutional Neural Networks for Magnetic Resonance Image reconstruction.

We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from timespace diagrams. We demonstrate this for a homogeneous road section using simulated vehicle trajectories and then validate it using real-world data from NGSIM. Our results show that with probe vehicle penetration levels as low as 5%, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds and reproduce realistic shockwave patterns, implying applicability in a variety of traffic conditions. We further discuss the model’s reconstruction mechanisms and confirm its ability to differentiate various traffic behaviors such as congested and free-flow traffic states, transition dynamics, and shockwave propagation.

[arXiv'18] Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks

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Deep-Learning convolutional auto-encoders applied to super-resolution microscopy data to localization image reconstruction.

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Multi-agent reconnaissance skill for Claude Code + Obsidian

Compressed Sensing signal decoding with DNN oracle on STM32

DSR

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The implementation of "GradNet: Unsupervised Deep Screened Poisson Reconstruction for Gradient-Domain Rendering"

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ShanghaiTech BME1312 project1: Deep learning Dynamic MRI Reconstruction

Reconstruction of images making them HDR using deep learning

DeepFovea++: Reconstruction and Super-Resolution for Natural Foveated Rendered Videos (PyTorch).