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<p>The field of RGB-D semantic segmentation has attracted considerable interest in recent times. The challenge is to develop an effective method for combining RGB images, which capture colour variations, with depth images, which provide robust information about object geometry regardless of lighting conditions. Treating both image types equally through the same convolution operator fails to take into account their inherent differences. Thus, in this paper, we propose a novel approach that combines a geometry-aware convolution (<i>GAConv</i>) module and a multiscale fusion module (MFM) with the aim of enhancing the performance of RGB-D image segmentation. The <i>GAConv</i> module effectively captures fine-grained geometric details from depth images, while the MFM module enables efficient integration of multi-scale features, allowing the network to utilise both spatial and semantic information. Extensive experimentation was conducted on the NYUv2 and SUN RGB-D datasets, wherein our model demonstrated consistent superiority over existing state-of-the-art methods in terms of pixel accuracy and mean intersection over union (mIoU).</p>

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GANet: geometry-aware network for RGB-D semantic segmentation

  • 期刊论文

  • 发表日期：

    2025年2月15日

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  • Chunqi Tian,
  • Weirong Xu,
  • Lizhi Bai,
  • Jun Yang,
  • Yanjun Xu

摘要

The field of RGB-D semantic segmentation has attracted considerable interest in recent times. The challenge is to develop an effective method for combining RGB images, which capture colour variations, with depth images, which provide robust information about object geometry regardless of lighting conditions. Treating both image types equally through the same convolution operator fails to take into account their inherent differences. Thus, in this paper, we propose a novel approach that combines a geometry-aware convolution (GAConv) module and a multiscale fusion module (MFM) with the aim of enhancing the performance of RGB-D image segmentation. The GAConv module effectively captures fine-grained geometric details from depth images, while the MFM module enables efficient integration of multi-scale features, allowing the network to utilise both spatial and semantic information. Extensive experimentation was conducted on the NYUv2 and SUN RGB-D datasets, wherein our model demonstrated consistent superiority over existing state-of-the-art methods in terms of pixel accuracy and mean intersection over union (mIoU).