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Improving 3D Point Cloud Segmentation Using Multimodal Fusion of Projected 2D Imagery Data: Improving 3D Point Cloud Segmentation Using Multimodal Fusion of Projected 2D Imagery Data
Linköping University, Department of Electrical Engineering, Computer Vision.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Semantic segmentation is a key approach to comprehensive image data analysis. It can be applied to analyze 2D images, videos, and even point clouds that contain 3D data points. On the first two problems, CNNs have achieved remarkable progress, but on point cloud segmentation, the results are less satisfactory due to challenges such as limited memory resource and difficulties in 3D point annotation. One of the research studies carried out by the Computer Vision Lab at Linköping University was aiming to ease the semantic segmentation of 3D point cloud. The idea is that by first projecting 3D data points to 2D space and then focusing only on the analysis of 2D images, we can reduce the overall workload for the segmentation process as well as exploit the existing well-developed 2D semantic segmentation techniques. In order to improve the performance of CNNs for 2D semantic segmentation, the study has used input data derived from different modalities. However, how different modalities can be optimally fused is still an open question. Based on the above-mentioned study, this thesis aims to improve the multistream framework architecture. More concretely, we investigate how different singlestream architectures impact the multistream framework with a given fusion method, and how different fusion methods contribute to the overall performance of a given multistream framework. As a result, our proposed fusion architecture outperformed all the investigated traditional fusion methods. Along with the best singlestream candidate and few additional training techniques, our final proposed multistream framework obtained a relative gain of 7.3\% mIoU compared to the baseline on the semantic3D point cloud test set, increasing the ranking from 12th to 5th position on the benchmark leaderboard.

Place, publisher, year, edition, pages
2019. , p. 70
Keywords [en]
deep learning, multimodal fusion, multimodality, semantic segmentation, point cloud segmentation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-157705ISRN: LiTH-ISY-EX--19/5190--SEOAI: oai:DiVA.org:liu-157705DiVA, id: diva2:1327473
Subject / course
Computer Engineering
Presentation
2019-02-28, Linköping, 13:00 (English)
Supervisors
Examiners
Available from: 2019-06-19 Created: 2019-06-19 Last updated: 2019-06-19Bibliographically approved

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CiteExportLink to record
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Citation style
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Output format
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