Deep Surface Reconstruction from Point Clouds with Visibility Information

Sulzer, Raphael; Landrieu, Loïc; Boulch, Alexandre; Marlet, Renaud; Vallet, Bruno

doi:10.48550/arxiv.2202.01810

Cited by 2 publications

(2 citation statements)

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“…Traditional surface reconstruction approaches are usually based on 3D Delaunay tetrahedralization [67][68][69] and are robust to medium levels of noise and outliers. Deep surface reconstruction (DSR) methods have been recently proposed for incorporating learned shape priors in the geometry generation [70][71][72][73]. In both cases, the artefact shape is generally reconstructed through a large number of polygons and triangles.…”

Section: Mesh Simplification and Texture Mappingmentioning

confidence: 99%

Analysing Key Steps of the Photogrammetric Pipeline for Museum Artefacts 3D Digitisation

et al. 2022

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In recent years, massive digitisation of cultural heritage (CH) assets has become a focus of European programmes and initiatives. Among CH settings, attention is reserved to the immense and precious museum collections, whose digital 3D reproduction can support broader non-invasive analyses and stimulate the realisation of more attractive and interactive exhibitions. The reconstruction pipeline typically includes numerous processing steps when passive techniques are selected to deal with object digitisation. This article presents some insights on critical operations, which, based on our experience, can rule the quality of the final models and the reconstruction times for delivering 3D heritage results, while boosting the sustainability of digital cultural contents. The depth of field (DoF) problem is explored in the acquisition phase when surveying medium and small-sized objects. Techniques for deblurring images and masking object backgrounds are examined relative to the pre-processing stage. Some point cloud denoising and mesh simplification procedures are analysed in data post-processing. Hints on physically-based rendering (PBR) materials are also presented as closing operations of the reconstruction pipeline. This paper explores these processes mainly through experiments, providing a practical guide, tricks, and suggestions when tackling museum digitisation projects.

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Section: Mesh Simplification and Texture Mappingmentioning

confidence: 99%

Analysing Key Steps of the Photogrammetric Pipeline for Museum Artefacts 3D Digitisation

et al. 2022

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“…A large body of recent work focuses on using neural networks to represent point samples of values that implicitly define surfaces, e.g., occupancy [6,7,14,22,26,28,32,36,37,46,52,53], signed distance field (SDF) [4,13,20,27,31,35,42,48,50,51,54,58], unsigned distance field [47], or level sets [15]. These approaches show high reconstruction fidelity due to their ability to represent the continuous domain of points, while remaining computationally tractable.…”

Section: Related Workmentioning

confidence: 99%

DeepMend: Learning Occupancy Functions to Represent Shape for Repair

Lamb¹,

Banerjee²,

Banerjee³

2022

Preprint

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We present DeepMend, a novel approach to reconstruct restorations to fractured shapes using learned occupancy functions. Existing shape repair approaches predict low-resolution voxelized restorations, or require symmetries or access to a pre-existing complete oracle. We represent the occupancy of a fractured shape as the conjunction of the occupancy of an underlying complete shape and the fracture surface, which we model as functions of latent codes using neural networks. Given occupancy samples from an input fractured shape, we estimate latent codes using an inference loss augmented with novel penalty terms that avoid empty or voluminous restorations. We use inferred codes to reconstruct the restoration shape. We show results with simulated fractures on synthetic and real-world scanned objects, and with scanned real fractured mugs. Compared to the existing voxel approach and two baseline methods, our work shows state-of-the-art results in accuracy and avoiding restoration artifacts over non-fracture regions of the fractured shape.

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Deep Surface Reconstruction from Point Clouds with Visibility Information

Cited by 2 publications

References 0 publications

Analysing Key Steps of the Photogrammetric Pipeline for Museum Artefacts 3D Digitisation

Analysing Key Steps of the Photogrammetric Pipeline for Museum Artefacts 3D Digitisation

DeepMend: Learning Occupancy Functions to Represent Shape for Repair

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