Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks
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Abstract (arXiv)
Many research works focus on leveraging the complementary geometric information of indoor depth sensors in vision tasks performed by deep convolutional neural networks, notably semantic segmentation. These works deal with a specific vision task known as "RGB-D Indoor Semantic Segmentation". The challenges and resulting solutions of this task differ from its standard RGB counterpart. This results in a new active research topic. The objective of this paper is to introduce the field of Deep Convolutional Neural Networks for RGB-D Indoor Semantic Segmentation. This review presents the most popular public datasets, proposes a categorization of the strategies employed by recent contributions, evaluates the performance of the current state-of-the-art, and discusses the remaining challenges and promising directions for future works.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 26 references that are in the map. First posted on arXiv 25 May 2021.
Builds on
16 of 26 references.
References · 16
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- 2018Depth-aware CNN for RGB-D Segmentationcited by 73
4 more
- 2018RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentationcited by 65
- 2016SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truthcited by 31
- 2020Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene Parsingcited by 24
- 20193D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentationcited by 6
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Citing papers per year: 2022: 1; 2023: 2.
3 papers.