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 provide better replicas of indoor spaces. Moreover, we suggest to focus only on permanent room elements such as walls, floors, and ceilings, to allow for easily customizable 3D models. We conduct experiments in a variety of indoor spaces to assess the texturing quality and speed of our method. The quantitative results and user study demonstrate that RoomRecon surpasses state-of-the-art methods in terms of texturing quality and on-device computation time.

PAnoramic Semantic Segmentation (PASS) is an important task in computer vision, as it enables semantic understanding of a 360° environment. Currently, most of existing works have focused on addressing the distortion issues in 2D panoramic images without considering spatial properties of indoor scene. This restricts PASS methods in perceiving contextual attributes to deal with the ambiguity when working with monocular images. In this paper, we propose a novel approach for indoor panoramic semantic segmentation. Unlike previous works, we consider the panoramic image as a composition of segment groups: oversampled segments, representing planar structures such as floors and ceilings, and under-sampled segments, representing other scene elements. To optimize each group, we first enhance over-sampled segments by jointly optimizing with a dense depth estimation task. Then, we introduce a transformer-based context module that aggregates different geometric representations of the scene, combined with a simple high-resolution branch, it serves as a robust hybrid decoder for estimating under-sampled segments, effectively preserving the resolution of predicted masks while leveraging various indoor geometric properties. Experimental results on both real-world (Stanford2D3DS, Matterport3D) and synthetic (Structured3D) datasets demonstrate the robustness of our framework, by setting new state-of-the-arts in almost evaluations, The code and updated results are available at: https://github.com/caodinhduc/vertical_relative_distance.