3D semantic segmentation using RGB and depth with PDCNet
3D semantic segmentation using RGB and depth with PDCNet
Pixel Difference Convolutional Network for RGB-D Semantic Segmentation
arXiv paper abstract https://arxiv.org/abs/2302.11951
arXiv PDF paper https://arxiv.org/pdf/2302.11951.pdf
RGB-D semantic segmentation can be advanced with convolutional neural networks due to the availability of Depth data.
Although objects cannot be easily discriminated by just the 2D appearance, with the local pixel difference and geometric patterns in Depth, they can be well separated in some cases.
... propose a Pixel Difference Convolutional Network (PDCNet) to capture detailed intrinsic patterns by aggregating both intensity and gradient information in the local range for Depth data and global range for RGB data, respectively.
... For ... Depth ... propose a Pixel Difference Convolution (PDC) to consider local and detailed geometric information in Depth data via aggregating both intensity and gradient information.
For ... RGB ... contribute a lightweight Cascade Large Kernel (CLK) to extend PDC, namely CPDC, to enjoy global contexts for RGB data and further boost performance.
... PDCNet achieves state-of-the-art performance for the semantic segmentation task.
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