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Generating 3D Scenes From Single RGB Images in Real-Time Using Neural Networks

The ability to reconstruct 3D scenes of environments is of great interest in a number of fields such as autonomous driving, surveillance, and virtual reality. However, traditional methods often rely on multiple cameras or sensor-based depth measurements to accurately reconstruct 3D scenes. In this thesis we propose an alternative, deep learning-based approach to 3D scene reconstruction for objects of interest, using nothing but single RGB images. We evaluate our approach using the Deep Object Pose Estimation (DOPE) neural network for object detection and pose estimation, and the NVIDIA Deep learning Dataset Synthesizer for synthetic data generation. Using two unique objects, our results indicate that it is possible to reconstruct 3D scenes from single RGB images within a few centimeters of error margin.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-43091
Date January 2021
CreatorsGrundberg, Måns, Altintas, Viktor
PublisherMalmö universitet, Institutionen för datavetenskap och medieteknik (DVMT)
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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