Stereo Visual Mesh for Generating Sparse Semantic Maps at High Frame Rates

Title: Stereo Visual Mesh for Generating Sparse Semantic Maps at High Frame Rates
Creator: Biddulph, Alexander; Houliston, Trent; Mendes, Alexandre; Chalup, Stephan
Relation: ICONIP 2023: Neural Information Processing. Proceedings of the Neural Information Processing. ICONIP 2023, Part VI, Volume 14452 (Changsha, China 20-23 November, 2023) p. 161-178
Publisher Link: http://dx.doi.org/10.1007/978-981-99-8076-5_12
Publisher: Springer
Resource Type: conference paper
Date: 2024
Description: The Visual Mesh is an input transform for deep learning that allows depth independent object detection at very high frame rates. The present study introduces a Visual Mesh based stereo vision method for sparse stereo semantic segmentation. A dataset of simulated 3D scenes was generated and used for training to show that the method is capable of processing high resolution stereo inputs to generate both left and right sparse semantic maps. The new stereo method demonstrated better classification accuracy than the corresponding monocular approach. The high frame rates and high accuracy may make the proposed approach attractive to fast-paced on-board robot or IoT applications.
Subject: deep learning; stereo vision; semantic segmentation; IoT applications
Identifier: http://hdl.handle.net/1959.13/1496719
Identifier: uon:54217
Identifier: ISBN:9789819980758
Language: eng
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