DeFi: Detection and Filling of Holes in Point Clouds Towards Restoration of Digitized Cultural Heritage Models

Tabib, Ramesh Ashok; Hegde, Dikshit; Anvekar, Tejas; Mudenagudi, Uma

doi:10.1109/iccvw60793.2023.00175

Cited by 5 publications

(1 citation statement)

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“…There is also the issue of point cloud inpainting-an active area of research in heritage preservation [42], automatic building models [43], and computer science more broadly [44]. Through the novel view synthesis ability of the underlying NeRFs in our approach, we did generatively fill particular areas of our point cloud where there were not enough reference images to construct a strong representation using an approach like photogrammetry-yet there were still substantial holes in our 3D models.…”

Section: Discussionmentioning

confidence: 99%

Automatic Removal of Non-Architectural Elements in 3D Models of Historic Buildings with Language Embedded Radiance Fields

Rusnak,

Pantoja-Rosero,

Kaplan

et al. 2024

Heritage

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Neural radiance fields have emerged as a dominant paradigm for creating complex 3D environments incorporating synthetic novel views. However, 3D object removal applications utilizing neural radiance fields have lagged behind in effectiveness, particularly when open set queries are necessary for determining the relevant objects. One such application area is in architectural heritage preservation, where the automatic removal of non-architectural objects from 3D environments is necessary for many downstream tasks. Furthermore, when modeling occupied buildings, it is crucial for modeling techniques to be privacy preserving by default; this also motivates the removal of non-architectural elements. In this paper, we propose a pipeline for the automatic creation of cleaned, architectural structure only point clouds utilizing a language embedded radiance field (LERF) with a specific application toward generating suitable point clouds for the structural integrity assessment of occupied buildings. We then validated the efficacy of our approach on the rooms of the historic Sion hospital, a national historic monument in Valais, Switzerland. By using our automatic removal pipeline on the point clouds of rooms filled with furniture, we decreased the average earth mover’s distance (EMD) to the ground truth point clouds of the physically emptied rooms by 31 percent. The success of our research points the way toward new paradigms in architectural modeling and cultural preservation.

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Section: Discussionmentioning

confidence: 99%