Semantic segmentation is a basic and long-standing research area. Depth images can enrich RGB (red-green-blue) images with their rich geometric information, so as to achieve accurate semantic segmentation. However, redundant information exists in RGB and depth images, and its handling has become an important problem. Filter group convolutions are widely used because they can eliminate redundant information and reduce computational complexity and parameter cost. Similarly, we propose a feature grouping mechanism network (FGMNet) using an attention mechanism and contextual information extraction for indoor scene semantic segmentation. First, modules of pyramid feature grouping attention and feature augmentation highlight the most useful information obtained by combining RGB and depth features. The enhanced features are then fed into a feature grouping contextual module. Results from extensive experiments on well-known indoor scene semantic segmentation datasets, NYUDv2 and SUN RGB-D, indicate that our FGMNet outperforms the most advanced existing methods in RGB-D semantic segmentation.

中文翻译：

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FGMNet：用于RGB-D室内场景语义分割的特征分组机制网络

语义分割是一个基础且长期存在的研究领域。深度图像可以利用其丰富的几何信息来丰富RGB（红绿蓝）图像，从而实现准确的语义分割。然而，RGB和深度图像中存在冗余信息，其处理成为一个重要问题。滤波器组卷积因其可以消除冗余信息并降低计算复杂度和参数成本而被广泛使用。类似地，我们提出了一种使用注意力机制和上下文信息提取进行室内场景语义分割的特征分组机制网络（FGMNet）。首先，金字塔特征分组注意力和特征增强模块突出了通过结合 RGB 和深度特征获得的最有用的信息。然后，增强的特征被输入到特征分组上下文模块中。对著名室内场景语义分割数据集 NYUDv2 和 SUN RGB-D 进行的大量实验结果表明，我们的 FGMNet 在 RGB-D 语义分割方面优于现有的最先进方法。