几篇关于RGBD语义分割文章的总结

  最近在调研3D算法方面的工作,整理了几篇多视角学习的文章。还没调研完,先写个大概。
  基于RGBD的语义分割的工作重点主要集中在如何将RGB信息和Depth信息融合,主要分为三类:省略。

1、(ICCV2017)《RDFNet: RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation》

用于室内语义分割的RGB-D多级残差特征融合
论文地址:https://openaccess.thecvf.com/content_iccv_2017/html/Park_RDFNet_RGB-D_Multi-Level_ICCV_2017_paper.html
代码:https://github.com/SeongjinPark/RDFNet
文章介绍:https://blog.csdn.net/u012113559/article/details/81363756

2、(2018 Arxiv)RedNet:Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation

论文地址:https://arxiv.org/abs/1806.01054
代码:https://github.com/JindongJiang/RedNet
文章介绍:https://blog.csdn.net/qq_41375318/article/details/104311597、
https://blog.csdn.net/qq_41375318/article/details/103451966

3、(ICIP2019)ACNet:使用注意力网络的RGBD图像语义分割方法

论文地址:https://arxiv.org/abs/1905.10089
代码:https://github.com/anheidelonghu/ACNet
文章介绍:https://blog.csdn.net/kevin_zhao_zl/article/details/100750591、
https://zhuanlan.zhihu.com/p/82193530

4、(NIPS2020)Deep Multimodal Fusion by Channel Exchanging

论文地址:https://arxiv.org/abs/2011.05005
代码:https://github.com/yikaiw/CEN
文章介绍:https://zhuanlan.zhihu.com/p/341959576、
https://blog.csdn.net/hongyuge/article/details/109632887
视频讲解:https://www.bilibili.com/video/BV1ya4y1W7Hf

5、(ECCV2020)Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation

论文地址:https://arxiv.org/abs/2007.09183
代码:https://github.com/charlesCXK/RGBD_Semantic_Segmentation_PyTorch
文章介绍:https://blog.csdn.net/sinat_17456165/article/details/107805136

6、(arxiv2021)GLPNet:Global-Local Propagation Network for RGB-D Semantic Segmentation

论文地址:https://arxiv.org/abs/2101.10801
代码:无
文章介绍:

7、(ACCV 2016) FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture

论文地址:https://www.semanticscholar.org/paper/FuseNet%3A-Incorporating-Depth-into-Semantic-via-CNN-Hazirbas-Ma/9360ce51ec055c05fd0384343792c58363383952
代码:https://github.com/tum-vision/fusenet
文章介绍:https://blog.csdn.net/u013841196/article/details/82939619

8、(SCIA2017)Multimodal Neural Networks: RGB-D for Segmantic Segmentation and Object Detection

论文地址:https://www.researchgate.net/publication/317803469_Multimodal_Neural_Networks_RGB-D_for_Semantic_Segmentation_and_Object_Detection
代码:
文章介绍:https://blog.csdn.net/qq_38316300/article/details/109546441

9、(3DV2019)3D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentation

论文地址:https://arxiv.org/abs/1910.01460
代码:
文章介绍:https://blog.csdn.net/cangafuture/article/details/113822865

10、(ICCV2017)3D Graph Neural Networks for RGBD Semantic Segmentation

论文地址:https://ieeexplore.ieee.org/document/8237818
代码:https://github.com/yanx27/3DGNN_pytorch
文章介绍:https://blog.csdn.net/P_LarT/article/details/88774811、https://blog.csdn.net/P_LarT/article/details/88774811

多模态Transformer

论文地址:https://arxiv.org/abs/1906.00295
代码:https://github.com/yaohungt/Multimodal-Transformer
论文介绍:https://zhuanlan.zhihu.com/p/84678022?from_voters_page=true、https://zhuanlan.zhihu.com/p/340113856、https://blog.csdn.net/zpainter/article/details/111867693

Transformer语义分割(SETR)

论文地址:https://arxiv.org/abs/2012.15840
代码:https://github.com/fudan-zvg/SETR
文章介绍:https://zhuanlan.zhihu.com/p/341768446

TransUNet:用于医学图像分割的Transformers强大编码器

论文地址:https://arxiv.org/abs/2102.04306
代码:https://github.com/Beckschen/TransUNet
文章介绍:https://blog.csdn.net/weixin_49627776/article/details/115710379

SegFormer:使用Transformer进行语义分割的简单高效设计

论文地址:https://arxiv.org/abs/2105.15203
代码:https://github.com/NVlabs/SegFormer
文章介绍:https://zhuanlan.zhihu.com/p/379054782

Swin-Unet:首个纯Transformer的医学图像分割网络

论文地址:https://arxiv.org/abs/2105.05537
代码:https://github.com/HuCaoFighting/Swin-Unet(目前未开源)
文章介绍:https://blog.csdn.net/amusi1994/article/details/116957208

学习跨模态深度表达用于多模态MR图像分割

地址:https://zhuanlan.zhihu.com/p/349918500

posted @ 2021-06-16 21:13  阁楼式的幻想  阅读(2110)  评论(0编辑  收藏  举报