Multimodal Neural Networks: RGB-D for Semantic Segmentation and Object Detection
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Many works deal with 2D object detection on the front-view 2D image plane.
“Many works [61], [91], [99][101], [106], [108], [109], [111], [117]-[120], [123] deal with the 2D object detection problem on the front-view 2D image plane.”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [61]
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References 40 · Cited by 42
- Authors
- Schneider, L., Jasch, M., Fröhlich, B., Weber, T., Franke, U., Pollefeys, M., & Rätsch, M.
- Affiliations
- ETH Zurich (CH)
- Reutlingen University (DE)
- Daimler (Germany) (DE)
- Microsoft (United States) (US)
- Corresponding author
- Schneider, Lukas
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 98-109
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2615011300
- Cited by
- 42
- References
- 40
- Citations by year
- 2025: 2 · 2024: 6 · 2023: 2 · 2022: 11 · 2021: 5 · 2020: 7 · 2019: 4 · 2018: 5
- FWCI
- 1.28
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Reutlingen University Academic Bibliography (Reutlingen University) (submittedVersion)
- 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
- multimodal neural networks, RGB-D, semantic segmentation, object detection, cityscape, GoogLeNet
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