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This project aims to develop approaches to automatically reconstruct an accurate floor plan of a house with multiple rooms using a LiDAR-enabled smartphone. Previous work in this area, such as those described in research papers on the models FloorNet, Floor-SP, and 4D Spatio-Temporal ConvNets, have guided us in formulating our approach. We propose to accomplish this task via a combination of human annotation and deep learning segmentation models. We have developed tools to facilitate rapid human annotation of key features (such as windows, walls, and corners). The human annotations will be complemented by annotations generated by a deep learning segmentation model that can identify and locate doors. Finally, we have designed a cloud computing architecture that can store these annotations and build a digital floor plan of the 3D scene. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) Sachan, Amit; Vakati, Girish; Mirza, Ifti; Burny, Mustafa; Nisbet, Parker; Bewley, Thomas; Alimo, Ryan; Beyhaghi, Pooriya (2022). Automatic Floorplan Reconstruction from RGB-D Images. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0Q52PS1 Due to the proprietary nature of the scripts and input and output data they are not available for download.
Type
dataset
Identifier
ark:/20775/bb38324777
Language
English
Subject
Capstone projects Neural networks (Computer science) Computer vision Amazon Web Service (AWS) Annotation Data Science & Engineering Master of Advanced Study (DSE MAS) Floor plan DSE MAS - 2022 Cohort
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