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 3 publications

(1 citation statement)

References 29 publications

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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%

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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Terrain point cloud inpainting via signal decomposition

Xie,

Wang

et al. 2024

Computers & Graphics

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Diffusion Transformer for point cloud registration: digital modeling of cultural heritage

An,

Zhou,

Zhou

et al. 2024

Herit Sci

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Digital modeling is an essential means for preserving and passing down historical culture within cultural heritage. Point cloud registration technology, by aligning point cloud data captured from multiple perspectives, enhances the accuracy of reconstructing the complex structures of artifacts and buildings and provides a reliable digital foundation for their protection, exhibition, and research. Due to the challenges posed by complex morphology, noise, and missing data when processing cultural heritage data, this paper proposes a point cloud registration method based on the Diffusion Transformer (PointDT). Compared to traditional methods, the Diffusion Transformer can better capture both the global features and local structures of point cloud data, more accurately capturing the geometric and semantic information of the target point cloud, thereby achieving precise digital reconstruction. In this study, we trained our method using indoor datasets such as 3DMatch and large-scale outdoor datasets like KITTI, and validated it on various cultural heritage datasets, including those of the Terracotta Warriors and heritage buildings. The results demonstrate that this method not only significantly improves accuracy but also shows advantages in computational efficiency.

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

Cited by 3 publications

References 29 publications

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

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

Terrain point cloud inpainting via signal decomposition

Diffusion Transformer for point cloud registration: digital modeling of cultural heritage

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