## 1 Introduction
Furthermore, machine learning (ML) is defined as \"an aspect of artificial intelligence that competently performs automation in the process of building analytical models that allow machines to adapt independently to new scenarios, enabling software to successfully predict and react to the deployment of scenarios based on past results\" [19]; or as a field of study that allows computers to learn autonomously without being explicitly programmed [20]. Deep learning (DL) is defined by [21] as models that can learn representations of data with multiple levels of abstraction and discover structure in large datasets. Even though there have been some partial Scan-to-BIM AI-based successes, no fully automated workflow is yet to be devised [22], meaning that it is plausible for AI to continue filling certain methodological gaps in the fabric of the Scan-to-BIM workflow. In this scenario, we propose an automatic 3D reconstruction method that provides AH specialists with an ML-based, 3D mesh reconstruction tool that automates the AH 3D modeling step of the general building process, thereby helping to reduce modeling errors, lightening project workloads, shortening completion times, costs and on-site resource expenditures, and facilitating the initial part of the Scan-to-BIM process. This means that the system automates 3D mesh reconstruction of AH from image/video inputs, moving beyond traditional, labor-intensive manual CAD methods. This approach directly reduces modeling errors and lightens project workloads by streamlining the geometric information generation process. Crucially, it replaces conventional, often problematic, point cloud inputs derived from expensive LiDAR or complex photogrammetry with more accessible photographic or video data. This shift intrinsically shortens completion times, lowers costs, and minimizes on-site resource expenditure. By leveraging Gaussian splatting (GS) and its noise-reducing variant, Mip-splatting for radiance field rendering (RFR), followed by surface-aligned Gaussian splatting for efficient 3D mesh reconstruction (SuGaR), the process becomes more efficient, particularly with Mip-splatting's superior noise reduction. This \"photo-to-mesh\" pipeline represents a crucial initial step in the Scan-to-BIM process for AH (HBIM), paving the way for AI-driven methodologies. We aim to achieve this by replacing point cloud input with video or photo entries in the Scan-to-BIM process, which we divided into two parts (photo-to-mesh and HBIM class binding), thus outlining a \"photo-to-mesh\" workflow. For this specific work, we focused on a \"video\" or \"photo-to-mesh\" pipeline for application in the AH virtual reconstruction process, where we used and tested three models in total. The first two, GS [23] and Mip-splatting [24] (a noise-reduction version of GS), both add a photo input step before the point cloud one. Additionally, a third model, SuGaR [25], takes the GS/Mip-splatting output point cloud and reconstructs a textured mesh. This mesh reconstruction step is the one immediately before the HBIM class binding step; we left the latter for future work. At a broader architectural theory level, we put another brick in the edifice by extending, from BIM to AI [12], a broader question on constructing architectural knowledge through semantic modeling systems and their automation. As claimed, BIM systems \"emphasize the use of a semantic construction of the digital model, not only as a means to modeling a building but as a cognitive system\" [12]. In other words, our leitmotif question was whether AI, and, more specifically, ML can change our way of thinking about AH. We therefore claim that AI application in AH, at a cognitive level, bridges the gap between \"by hand\" work and automatic building reconstruction. In turn, this bypasses \"manual\" CAD modeling and thus changes AH's internal nature. Henceforth, this paper is structured in seven sections. The state-of-the-art (SOTA) section briefly describes foundational, previous, and related works.