Multi-view Continuous Structured Light Scanning

Groh, Fabian; Resch, Benjamin; Lensch, Hendrik P. A.

doi:10.1007/978-3-319-66709-6_30

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Reconstruction of a 3D scan [GRL17] with 1.4M samples (only 10k visualized). Our mesh is adaptive to the scan's resolution and covers even the empty areas under the chin and arms.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical Quad Meshing of 3D Scanned Surfaces

Bukenberger

Lensch

2018

Computer Graphics Forum

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In this paper we present a novel method to reconstruct watertight quad meshes on scanned 3D geometry. There exist many different approaches to acquire 3D information from real world objects and sceneries. Resulting point clouds depict scanned surfaces as sparse sets of positional information. A common downside is the lack of normals, connectivity or topological adjacency data which makes it difficult to actually recover a meaningful surface. The concept described in this paper is designed to reconstruct a surface mesh despite all this missing information. Even when facing varying sample density, our algorithm is still guaranteed to produce watertight manifold meshes featuring quad faces only. The topology can be set‐up to follow superimposed regular structures or align naturally to the point cloud's shape. Our proposed approach is based on an initial divide and conquer subsampling procedure: Surface samples are clustered in meaningful neighborhoods as leafs of a kd‐tree. A representative sample of the surface neighborhood is determined for each leaf using a spherical surface approximation. The hierarchical structure of the binary tree is utilized to construct a basic set of loose tiles and to interconnect them. As a final step, missing parts of the now coherent tile structure are filled up with an incremental algorithm for locally optimal gap closure. Disfigured or concave faces in the resulting mesh can be removed with a constrained smoothing operator.

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“…Reconstruction of a 3D scan [GRL17] with 1.4M samples (only 10k visualized). Our mesh is adaptive to the scan's resolution and covers even the empty areas under the chin and arms.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical Quad Meshing of 3D Scanned Surfaces

Bukenberger

Lensch

2018

Computer Graphics Forum

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“…Weinmann et al approach paved the way for the removal of time-consuming registration/stitching methods for multiple measurements whilst maximising object coverage. Following this work, Groh et al [112] developed a multi-view fringe projection system composed of two cameras (moving along an arc-shaped gantry) and three projectors. Once again, the degree of coverage of the measured objects was maximised and the time for the acquisitions was largely reduced.…”

Section: Multi-view Measurement Systemsmentioning

confidence: 99%

Smart optical coordinate and surface metrology

Catalucci

Thompson

Eastwood

et al. 2022

Meas. Sci. Technol.

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Manufacturing has recently experienced increased adoption of optimised and fast solutions for checking product quality during fabrication, allowing for manufacturing times and costs to be significantly reduced. Due to the integration of machine learning algorithms, advanced sensors and faster processing systems, smart instruments can autonomously plan measurement pipelines, perform decisional tasks and trigger correctional actions as required. In this paper, we summarise the state of the art in smart optical metrology, covering the latest advances in integrated intelligent solutions in optical coordinate and surface metrology, respectively for the measurement of part geometry and surface texture. Within this field, we include the use of a priori knowledge and implementation of machine learning algorithms for measurement planning optimisation. We also cover the development of multi-sensor and multi-view instrument configurations to speed up the measurement process, as well as the design of novel feedback tools for measurement quality evaluation.

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“…Such a low resolution enables single object classification, where the primary information is in the global shape characteristics [30]. Dense, complex 3D scenes, however, typically consist of millions of points [2,8]. Extending previous learning-based approaches to effectively process larger point clouds has been infeasible (Figure 1 (c)).…”

Section: Introductionmentioning

confidence: 99%

Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)

Groh,

Wieschollek,

Lensch

2018

Preprint

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Traditional convolution layers are specifically designed to exploit the natural data representation of images -a fixed and regular grid. However, unstructured data like 3D point clouds containing irregular neighborhoods constantly breaks the grid-based data assumption. Therefore applying best-practices and design choices from 2D-image learning methods towards processing point clouds are not readily possible. In this work, we introduce a natural generalization flex-convolution of the conventional convolution layer along with an efficient GPU implementation. We demonstrate competitive performance on rather small benchmark sets using fewer parameters and lower memory consumption and obtain significant improvements on a million-scale real-world dataset. Ours is the first which allows to efficiently process 7 million points concurrently.

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Multi-view Continuous Structured Light Scanning

Cited by 5 publications

References 22 publications

Hierarchical Quad Meshing of 3D Scanned Surfaces

Hierarchical Quad Meshing of 3D Scanned Surfaces

Smart optical coordinate and surface metrology

Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)

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