Efficiently Distributed Watertight Surface Reconstruction

Caraffa, Laurent; Marchand, Yanis; Brédif, Mathieu; Vallet, Bruno

doi:10.1109/3dv53792.2021.00150

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Polygonal models represent the object geometry derived from the photogrammetric pipeline. 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].…”

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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“…Volumetric segmentation is a subcategory of indicator function that classifies whether a voxel is occupied or empty with a confidence level using octrees [33] or Delaunay triangulation [34]. It can be scaled to arbitrarily large point clouds by distributing the surface reconstruction problem [35]. Some works on surface reconstruction have focused on identifying drive-able zones for robot navigation through the creation of simplistic 3D models of roads and buildings [36].…”

Section: Volumetric Segmentationmentioning

confidence: 99%

AdaSplats: Adaptive Splatting of Point Clouds for Accurate 3D Modeling and Real-Time High-Fidelity LiDAR Simulation

Richa

Deschaud²,

Goulette

et al. 2022

Remote Sensing

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LiDAR sensors provide rich 3D information about their surroundings and are becoming increasingly important for autonomous vehicles tasks such as localization, semantic segmentation, object detection, and tracking. Simulation accelerates the testing, validation, and deployment of autonomous vehicles while also reducing cost and eliminating the risks of testing in real-world scenarios. We address the problem of high-fidelity LiDAR simulation and present a pipeline that leverages real-world point clouds acquired by mobile mapping systems. Point-based geometry representations, more specifically splats (2D oriented disks with normals), have proven their ability to accurately model the underlying surface in large point clouds, mainly with uniform density. We introduce an adaptive splat generation method that accurately models the underlying 3D geometry to handle real-world point clouds with variable densities, especially for thin structures. Moreover, we introduce a fast LiDAR sensor simulator, working in the splatted model, that leverages the GPU parallel architecture with an acceleration structure while focusing on efficiently handling large point clouds. We test our LiDAR simulation in real-world conditions, showing qualitative and quantitative results compared to basic splatting and meshing techniques, demonstrating the interest of our modeling technique.

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Deep Surface Reconstruction from Point Clouds with Visibility Information

Sulzer

Landrieu

Boulch³

et al. 2022

2022 26th International Conference on Pattern Recognition (ICPR)

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Efficiently Distributed Watertight Surface Reconstruction

Abstract: Figure 1: Main steps of the distributed algorithm. From left to right: multiple input point clouds (aerial and terrestrial), partial view of the distributed Delaunay triangulation, large and close-up view of the resulting watertight surface (3D tiles are in different colours).

Cited by 3 publications

References 19 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

AdaSplats: Adaptive Splatting of Point Clouds for Accurate 3D Modeling and Real-Time High-Fidelity LiDAR Simulation

Deep Surface Reconstruction from Point Clouds with Visibility Information

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