DeblurGANv2
Pix2Vox
| DeblurGANv2 | Pix2Vox | |
|---|---|---|
| 1 | 1 | |
| 976 | 439 | |
| 2.8% | - | |
| 0.0 | 3.5 | |
| almost 2 years ago | 3 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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DeblurGANv2
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Implementing a CNN on an FPGA
I am trying to implement an image deblurring model (DeblurGANv2) The generator takes blurred images as an input and outputs the corrected image, the discriminator helps in training the generator. The model has been trained and I have run and deblurred images on a GPU. I am completely new to the domain of Machine learning and I am trying to implement this on an FPGA. I wanted your guys help on how to do this. The .h5 files contains the weights of the NN, how do I figure out its structure and put it on my FPGA. Can you please suggest some tools that might help or github links for people who have implemented similar stuff.
Pix2Vox
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[need help] I am trying to do 3d object reconstruction using rgbd images from kinnect device.
I am trying to achieve what this paper is doing https://github.com/hzxie/Pix2Vox but this paper is using only RGB images. I want to introduce depth in it as well. So for example, I will place a cup on table and get its different snapshots from different orientations using the Kinnect 2 device. These images will be passed through some pipeline of algorithms (can be ml or dl) to get Voxels (cup or object as a volume).
What are some alternatives?
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
Pointnet_Pointnet2_pytorch - PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
XVFI - [ICCV 2021, Oral 3%] Official repository of XVFI
IGR - Implicit Geometric Regularization for Learning Shapes
pi-GAN-pytorch - Implementation of π-GAN, for 3d-aware image synthesis, in Pytorch
unsupervised-depth-completion-visual-inertial-odometry - Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)
NAFNet - The state-of-the-art image restoration model without nonlinear activation functions.
2dimageto3dmodel - We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.
Ghost-DeblurGAN - This is a lightweight GAN developed for real-time deblurring. The model has a super tiny size and a rapid inference time. The motivation is to boost marker detection in robotic applications, however, you may use it for other applications definitely.
PIFu - This repository contains the code for the paper "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization"