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Found 3 papers, 0 papers with code
NeB-SLAM: Neural Blocks-based Salable RGB-D SLAM for Unknown Scenes
However, these methods necessitate the size of the scene as input, which is impractical for unknown scenes.
Pixel Difference Convolutional Network for RGB-D Semantic Segmentation
For the Depth branch, we propose a Pixel Difference Convolution (PDC) to consider local and detailed geometric information in Depth data via aggregating both intensity and gradient information.
Ranked #13 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
DCANet: Differential Convolution Attention Network for RGB-D Semantic Segmentation
A two-branch network built with DCA and EDCA, called Differential Convolutional Network (DCANet), is proposed to fuse local and global information of two-modal data.
Ranked #13 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
