Cited by 330Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningAdvanced Vision and Imaging

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Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis

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Seichter, Daniel, Kohler, Mona, Lewandowski, Benjamin, Wengefeld, Tim, Gross, Horst-Michael·2021·2021 IEEE International Conference on Robotics and Automation (ICRA)

DOI 10.1109/icra48506.2021.9561675

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Abstract

Analyzing scenes thoroughly is crucial for mobile robots acting in different environments. Semantic segmentation can enhance various subsequent tasks, such as (semantically assisted) person perception, (semantic) free space detection, (semantic) mapping, and (semantic) navigation. In this paper, we propose an efficient and robust RGB-D segmentation approach that can be optimized to a high degree using NVIDIA TensorRT and, thus, is well suited as a common initial processing step in a complex system for scene analysis on mobile robots. We show that RGB-D segmentation is superior to processing RGB images solely and that it can still be performed in real time if the network architecture is carefully designed. We evaluate our proposed Efficient Scene Analysis Network (ESANet) on the common indoor datasets NYUv2 and SUNRGB-D and show that we reach state-of-the-art performance while enabling faster inference. Furthermore, our evaluation on the outdoor dataset Cityscapes shows that our approach is suitable for other areas of application as well. Finally, instead of presenting benchmark results only, we also show qualitative results in one of our indoor application scenarios.

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Authors: Seichter, D., Kohler, M., Lewandowski, B., Wengefeld, T., & Gross, H.-M.
Affiliations: Technische Universität Ilmenau (DE)
Technische Universität Ilmenau,Neuroinformatics and Cognitive Robotics Lab,Ilmenau,Germany,98693
ORCID: Seichter, Daniel
Gross, Horst-Michael
Venue: 2021 IEEE International Conference on Robotics and Automation (ICRA)
Publisher: IEEE
Published: 2021-05-30
Pages: 13525-13531
ISSN: 1050-4729 (ISSN-L)
DOI: 10.1109/icra48506.2021.9561675
OpenAlex: W3100969133
Cited by: 330
References: 49
Citations by year: 2026: 36 · 2025: 76 · 2024: 92 · 2023: 70 · 2022: 40 · 2021: 16
FWCI: 17.83
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Ministry of Education
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
Keywords: RGB-D semantic segmentation, mobile robots, real-time inference, semantic mapping, semantic navigation, person perception, NYUv2, cityscape

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Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis

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