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3D Object Super-Resolution

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3D object super-resolution is the task of up-sampling 3D objects.

<span style="color:grey; opacity: 0.6">( Image credit: Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/CreativeCodingLab/DeepIllumination

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/DAMI211/srgan

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/DanGOTO100/SCADA-GAN-Synthetic-Generation

Generative Adversarial Networks

https://github.com/DaneyAlex5/Deep-Zooming-of-Images-using-SRGAN

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/Daniahblog/SinGAN

SinGAN: Learning a Generative Model from a Single Natural Image

https://github.com/DanielLongo/AdversarialTrain

cGANs with Projection Discriminator

https://github.com/DanielLongo/CapsGAN

Generative Adversarial Networks

https://github.com/DanielLongo/GANs

cGANs with Projection Discriminator

https://github.com/DanielLongo/cGANs

cGANs with Projection Discriminator

https://github.com/Davidwang96/my-ZSSR

"Zero-Shot" Super-Resolution using Deep Internal Learning

https://github.com/Dawir7/SupResDiffGAN

SupResDiffGAN a new approach for the Super-Resolution task

https://github.com/DerrickXuNu/OpenCOOD

PIXOR: Real-time 3D Object Detection from Point Clouds

https://github.com/Dictator890/ESRGAN-Android-Demo

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/DivJAth/DeepLearning5922

Residual Dense Network for Image Super-Resolution

https://github.com/Divsigma/2020-cs213n

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/DmitryUlyanov/deep-image-prior

Deep Image Prior

https://github.com/DmitryUlyanov/texture_nets

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/Dou-Yu-xuan/FSRNet

FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors

https://github.com/Dragoniss/mindspore-phase2-Deep-Fourier-Upsampling/blob/main/Fourier-Upsampling.py

Deep Fourier Up-Sampling

https://github.com/DwightFoster/Pytorch-TecoGan

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/EbadSyed/spadRGBD

Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

https://github.com/EdgeOfNetwork/Tiger_proj

Generative Adversarial Networks

https://github.com/Edresson/ZS-TTS-Evaluation

HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech Synthesis

https://github.com/Eisbaer8/DeepGTAV

Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?

https://github.com/EliasNehme/DeepSTORM3D

DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learning

https://github.com/ErikFaustmann/styletransfer

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/FNNDSC/pl-surfaces-fetus

Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels

https://github.com/FaceOnLive/License-Plate-Recognition-SDK-Android

Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution

https://github.com/FeliMe/multimodal_style_transfer

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/Feynman1999/MgeEditing

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

https://github.com/Feynman1999/basicVSR_mge

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

https://github.com/Fickle519/stockforecastingmodelbasedon_XGboost

XGBoost: A Scalable Tree Boosting System

https://github.com/FirstPotatoMan/TensorflowJS_ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/Fivefold/SRCNN

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/FlorentinCDX/pytorch-mnist-GAN

Generative Adversarial Networks

https://github.com/FnSK4R17s/GAN

Generative Adversarial Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.