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Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments
- Publication Year :
- 2022
-
Abstract
- Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about individual objects as well as the general scene are missing but required for many real-world applications. However, solving multiple tasks separately is expensive and cannot be accomplished in real time given limited computing and battery capabilities on a mobile platform. In this paper, we propose an efficient multi-task approach for RGB-D scene analysis~(EMSANet) that simultaneously performs semantic and instance segmentation~(panoptic segmentation), instance orientation estimation, and scene classification. We show that all tasks can be accomplished using a single neural network in real time on a mobile platform without diminishing performance - by contrast, the individual tasks are able to benefit from each other. In order to evaluate our multi-task approach, we extend the annotations of the common RGB-D indoor datasets NYUv2 and SUNRGB-D for instance segmentation and orientation estimation. To the best of our knowledge, we are the first to provide results in such a comprehensive multi-task setting for indoor scene analysis on NYUv2 and SUNRGB-D.<br />Comment: To be published in IEEE International Joint Conference on Neural Networks (IJCNN) 2022
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2207.04526
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/IJCNN55064.2022.9892852