Semantic Segmentation for Real-Time 3D Scene Graph Construction
2024 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Student thesis
Abstract [en]
The rapid advancements in computer vision technology have paved the way for significant innovations across various fields, such as augmented reality, virtual reality and robotics. Part of these advancements is the generation of 3D scene graphs, which enable machines to semantically understand and structure 3D environments, to enhance perception of the physical world. This thesis explores the integration of real-time semantic segmentation into the Hydra 3D framework to construct dynamic and accurate 3D scene graphs. Real-time semantic segmentation is crucial for various applications, providing pixel-level scene understanding, essential for tasks such as obstacle avoidance in robotics, and enhancing augmented and virtual reality applications.
The research investigates the challenges and potential of embedding state-of-the-art real-time semantic segmentation algorithms into the Hydra framework, which traditionally relies on preprocessed input data. By conducting this integration, the study aims to enhance the real-time capabilities of Hydra, enabling it to process live data and generate 3D scene graphs efficiently. Conducted in collaboration with the company “Ericsson”, this research compares the performance and suitability of various segmentation algorithms, seeking to optimize Hydra’s functionality for real-world applications.
The study addresses three primary research questions, the integration methods of real-time segmentation within Hydra, the identification of suitable algorithms, and the performance comparison between the enhanced Hydra framework and its original configuration. The research provides a proof of concept for integrating real-time semantic segmentation into Hydra, demonstrating potential system improvements.
The findings contribute to the broader field of computer vision, by proposing a robust framework for real-time 3D scene graph construction, with implications for advanced applications in the field of robotics. This work not only highlights the growing importance of integrating advanced computer vision techniques into practical frameworks, but also sets the stage for future innovations in autonomous systems and immersive digital experiences.
Place, publisher, year, edition, pages
2024. , p. 66
Keywords [en]
real-time, semantic segmentation, Hydra framework, Computer Vision, Robotics, 3d scene graph, PP-Light
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:mau:diva-71733OAI: oai:DiVA.org:mau-71733DiVA, id: diva2:1907600
External cooperation
Ericsson
Educational program
TS Computer Science: Applied Data Science
Supervisors
Examiners
2024-10-232024-10-232024-10-23Bibliographically approved