Evaluation of multimodal semantic segmentation using RGB-D data

Jiesi Hu; Ganning Zhao; Suya You; C. C. Jay Kuo

doi:10.1117/12.2587991

12 April 2021 Evaluation of multimodal semantic segmentation using RGB-D data

Jiesi Hu, Ganning Zhao, Suya You, C. C. Jay Kuo

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117461I (2021) https://doi.org/10.1117/12.2587991
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Our goal is to develop stable, accurate, and robust semantic scene understanding methods for wide-area scene perception and understanding, especially in challenging outdoor environments. To achieve this, we are exploring and evaluating a range of related technology and solutions, including AI-driven multimodal scene perception, fusion, processing, and understanding. This work reports our efforts on the evaluation of a state-of-the-art approach for semantic segmentation with multiple RGB and depth sensing data. We employ four large datasets composed of diverse urban and terrain scenes and design various experimental methods and metrics. In addition, we also develop new strategies of multi-datasets learning to improve the detection and recognition of unseen objects. Extensive experiments, implementations, and results are reported in the paper.

Conference Presentation

Citation Download Citation

Jiesi Hu, Ganning Zhao, Suya You, and C. C. Jay Kuo "Evaluation of multimodal semantic segmentation using RGB-D data", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117461I (12 April 2021); https://doi.org/10.1117/12.2587991

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