Real-time fully incremental scene understanding on mobile platforms
Wald J, Tateno K, Sturm J, Navab N, Tombari F (2018)
Publication Type: Journal article
Publication year: 2018
Journal
Book Volume: 3
Pages Range: 3402-3409
Article Number: 8403286
Journal Issue: 4
Abstract
We propose an online RGB-D based scene understanding method for indoor scenes running in real time on mobile devices. First, we incrementally reconstruct the scene via simultaneous localization and mapping and compute a three-dimensional (3-D) geometric segmentation by fusing segments obtained from each input depth image in a global 3-D model. We combine this geometric segmentation with semantic annotations to obtain a semantic segmentation in form of a semantic map. To accomplish efficient semantic segmentation, we encode the segments in the global model with a fast incremental 3-D descriptor and use a random forest to determine its semantic label. The predictions from successive frames are then fused to obtain a confident semantic class across time. As a result, the overall method achieves an accuracy that gets close to the most state-of-the-art 3-D scene understanding methods while being much more efficient, enabling real-time execution on low-power embedded systems.
Involved external institutions
Germany (DE)How to cite
APA:
Wald, J., Tateno, K., Sturm, J., Navab, N., & Tombari, F. (2018). Real-time fully incremental scene understanding on mobile platforms. IEEE Robotics and Automation Letters, 3(4), 3402-3409. https://dx.doi.org/10.1109/LRA.2018.2852782
MLA:
Wald, Johanna, et al. "Real-time fully incremental scene understanding on mobile platforms." IEEE Robotics and Automation Letters 3.4 (2018): 3402-3409.
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