Abstract
This paper focuses on indoor semantic segmentation based on RGB-D images. Semantic segmentation is a pixel-level classification task that has made steady progress based on fully convolutional networks (FCNs). However, we find there is still room for improvements in the following three aspects. The first is related to multi-scale feature extraction. Recent state-of-the-art works forcibly concatenate multi-scale feature representations extracted by spatial pyramid pooling, dilated convolution or other architectures, regardless of the spatial extent for each pixel. The second is regarding RGB-D modal fusion. Most successful methods treat RGB and depth as two separate modalities and force them to be joined together regardless of their different contributions to the final prediction. The final aspect is about the modeling ability of extracted features. Due to the “local grid” defined by the receptive field, the learned feature representation lacks the ability to model spatial dependencies. In addition to these modules, we design a depth estimation module to encourage the RGB network to extract more effective features. To solve the above challenges, we propose four modules to address them: scale-aware module, modality-aware module, attention module and depth estimation module. Extensive experiments on the NYU-Depth v2 and SUN RGB-D datasets demonstrate that our method is effective against RGB-D indoor semantic segmentation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.