RoomRecon: High-Quality Textured Room Layout Reconstruction on Mobile Devices
Seok Joon Kim, Dinh Duc Cao, Federica Spinola, Se Jin Lee, Kyu Sung Cho

TL;DR
RoomRecon is a real-time, mobile-friendly 3D room reconstruction system that enhances texturing quality using AR-guided image capture and AI, focusing on permanent room elements for better realism.
Contribution
It introduces a two-phase texturing pipeline combining AR-guided image capture and generative AI, improving on existing methods for mobile indoor scene reconstruction.
Findings
Outperforms state-of-the-art in texturing quality
Operates efficiently on mobile devices
Receives positive user feedback in experiments
Abstract
Widespread RGB-Depth (RGB-D) sensors and advanced 3D reconstruction technologies facilitate the capture of indoor spaces, improving the fields of augmented reality (AR), virtual reality (VR), and extended reality (XR). Nevertheless, current technologies still face limitations, such as the inability to reflect minor scene changes without a complete recapture, the lack of semantic scene understanding, and various texturing challenges that affect the 3D model's visual quality. These issues affect the realism required for VR experiences and other applications such as in interior design and real estate. To address these challenges, we introduce RoomRecon, an interactive, real-time scanning and texturing pipeline for 3D room models. We propose a two-phase texturing pipeline that integrates AR-guided image capturing for texturing and generative AI models to improve texturing quality and…
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