PP-LinkNet：通过多阶段训练提高高分辨率卫星图像的语义分割
该存储库包含代码。如果您觉得此存储库有用，请引用我们的论文：
inproceedings{AnTran_ACMMM_2020,
author = {Tran, An and Zonoozi, Ali and Varadarajan, Jagannadan and Kruppa, Hannes},
title = {PP-LinkNet: Improving Semantic Segmentation of High Resol
3D-迷你网-TF2
可在此处找到3D-MiniNet Tensorflow和Pytorch版本的正式实现： :
该论文可以在这里找到： :
引文
如果您发现3D-MiniNet有用，请考虑引用：
article{alonso2020MiniNet3D,
title={3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmen
FastFCN：重新思考骨干中的扩张卷积以进行语义分割
FastFCN的正式实施：重新思考骨干中的扩张卷积以进行语义分割。 一个更快，更强，更轻的语义分割框架，实现了最先进的性能和超过3倍的加速。
inproceedings{wu2019fastfcn,
title = {FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation},
author = {Wu, Hu
FastFCN：重新考虑骨干中的扩张卷积以进行语义分割
FastFCN的正式实施：重新思考骨干中的扩张卷积以进行语义分割。 一个更快，更强，更轻的语义分割框架，实现了最先进的性能和超过3倍的加速。
inproceedings{wu2019fastfcn,
title = {FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation},
author = {Wu, Hu
轻型RefineNet（在PyTorch中）
该存储库提供了Light-Weight RefineNet for Real-Time Semantic Segmentation的论文Light-Weight RefineNet for Real-Time Semantic Segmentation官方模型，可
Light-Weight RefineNet for Real-Time Semantic Segmentation
Vladimir Nekrasov, Chunhua Shen, I
DeepBackSub
Virtual Video Device for Background Replacement with Deep Semantic Segmentation
(Credits for the nice backgrounds to and )
In these modern times where everyone is sitting at home and skype-ing/zoom-ing/webrtc-ing all the time, I was a bi
3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans (CVPR2019 Oral)
We present 3D-SIS, a new framework for 3d instance segmentation.
Data Generation
Data generation code is detailed in .
Download Traininig Data
The training data we generated is
