The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Multimodal deep learning for robust RGB-D object recognition
Open accessAbstract
Robust object recognition is a crucial ingredient of many, if not all, real-world robotics applications. This paper leverages recent progress on Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture for object recognition. Our architecture is composed of two separate CNN processing streams - one for each modality - which are consecutively combined with a late fusion network. We focus on learning with imperfect sensor data, a typical problem in real-world robotics tasks. For accurate learning, we introduce a multi-stage training methodology and two crucial ingredients for handling depth data with CNNs. The first, an effective encoding of depth information for CNNs that enables learning without the need for large depth datasets. The second, a data augmentation scheme for robust learning with depth images by corrupting them with realistic noise patterns. We present state-of-the-art results on the RGB-D object dataset [15] and show recognition in challenging RGB-D real-world noisy settings.
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
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Learning Hierarchical Features for Scene Labeling
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Azizpour, H., Razavian, A. S., Sullivan, J., Maki, A., & Carlsson, S. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2500224 · Cited by 301
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Cited by
Perception, Planning, Control, and Coordination for Autonomous Vehicles
Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D., & Ang, M. (2017) · Machines · doi:10.3390/machines5010006 · Cited by 657
LIDAR–camera fusion for road detection using fully convolutional neural networks
Caltagirone, L., Bellone, M., Svensson, L., & Wahde, M. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.11.002 · Cited by 370
Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks
Audebert, N., Le Saux, B., & Lefèvre, S. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54181-5_12 · Cited by 367
Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection
Chen, H., & Li, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00322 · Cited by 336
Deep learning in robotics: A review of recent research
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Metadata
- Authors
- Eitel, A., Springenberg, J. T., Spinello, L., Riedmiller, M., & Burgard, W.
- Venue
- 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2015-09
- Pages
- 681-687
- OpenAlex
- W1012273433
- Cited by
- 631
- References
- 7
- FWCI
- 28.68
- Type
- conference paper
- Language
- en
- Open access
- green
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- Eitel, A., Springenberg, J. T., Spinello, L., Riedmiller, M., & Burgard, W. (2015). Multimodal deep learning for robust RGB-D object recognition. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros.2015.7353446
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