Extraction of Reliable Primitives from Unorganized Point Clouds

Tran, Trung-Thien; Cao, Van-Toan; Laurendeau, Denis

doi:10.1007/s13319-015-0076-1

Cited by 22 publications

(11 citation statements)

References 19 publications

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“…Instead of fitting the surface, missing points are inferred from a spline curve guided tensor voting mechanism in [26]. Primitive shapes are extracted in [31] to help with hole-filling.…”

Section: B Point Cloud Denoising and Inpaintingmentioning

confidence: 99%

Denoising and Inpainting for Point Clouds Compressed by V-PCC

Cao

Cosman

2021

IEEE Access

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With the development of immersive video, the quality of compressed 3D content has become an important issue. Video-based Point Cloud Compression (V-PCC) is a popular compression method for point cloud sequences; it achieves the highest quality among MPEG proposals. Compressed point clouds suffer from various artifacts when a high quantization parameter (QP) is used. Examining the causes and types of V-PCC artifacts that occur, we propose a framework to remove the highly noticeable outlier and crack artifacts caused by V-PCC so as to improve compressed point cloud visual quality. A subjective experiment showed that our approach provides significantly improved visual quality, and the improvement becomes more obvious with increasing QP values. Objective evaluation with point-to-point Mean Squared Error (p2p-MSE) shows our proposed method can improve point cloud quality and provides competitive results with lower complexity compared with other methods for point cloud outlier removal and inpainting.

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“…Instead of fitting the surface, missing points are inferred from a spline curve guided tensor voting mechanism in [26]. Primitive shapes are extracted in [31] to help with hole-filling.…”

Section: B Point Cloud Denoising and Inpaintingmentioning

confidence: 99%

Denoising and Inpainting for Point Clouds Compressed by V-PCC

Cao

Cosman

2021

IEEE Access

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“…Its verification step scores and ranks model candidates by point features such as normals. Tran et al [126] performed a primitive-type recognition step before extracting primitives. This method identifies the primitive type by finding the best-fit primitive model that has the smallest fitting error.…”

Section: A Shape Primitivesmentioning

confidence: 99%

Geometric Primitives in LiDAR Point Clouds: A Review

Xia

Chen

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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To the best of our knowledge, the most recent light detection and ranging (lidar)-based surveys have been focused only on specific applications such as reconstruction and segmentation, as well as data processing techniques based on a specific platform, e.g., mobile laser. However, in this article, lidar point clouds are understood from a new and universal perspective, i.e., geometric primitives embedded in versatile objects in the physical world. In lidar point clouds, the basic unit is the point coordinate. Geometric primitives that consist of a group of discrete points may be viewed as one kind of abstraction and representation of lidar data at the entity level. We categorize geometric primitives into two classes: shape primitives, e.g., lines, surfaces, and volumetric shapes, and structure primitives, represented by skeletons and edges. In recent years, many efforts from different communities, such as photogrammetry, computer vision, and computer graphics, have been made to finalize geometric primitive detection, regularization, and in-depth applications. Interpretations of geometric primitives from multiple disciplines try to convey the significance of geometric primitives, the latest processing techniques regarding geometric primitives, and their potential possibilities in the context of lidar point clouds. To this end, primitive-based applications are reviewed with an emphasis on object extraction and reconstruction to clearly show the significances of this article. Next, we survey and compare methods for geometric primitive extraction and then review primitive regularization methods that add real-world constrains to initial primitives. Finally, we summarize the challenges, expected applications, and describe possible future for primitive extraction methods that can achieve globally optimal results efficiently, even with disorganized, uneven, noisy, incomplete, and large-scale lidar point clouds.

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“…Primitive extraction can be based on fitting profiles [1], quadratic surfaces [29] or freeform surfaces [30]. But most of the time, primitive extraction uses curvature analysis, after a mesh segmen-135 tation [2], with a distribution [31], or even with outlier handling [32]. Since two primitives with identical parameters are not distinguished by their curvature, these methods cannot directly handle primitive positions.…”

Section: Primitive Type Detectionmentioning

confidence: 99%

Analysis of digitized 3D mesh curvature histograms for reverse engineering

Gauthier

Puech

Bénière³

et al. 2017

Computers in Industry

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Today, it has become more frequent and reasonably easy to digitize the surface of 3D objects. However, the obtained results are often inaccurate and noisy. In this paper, we present an efficient method to analyze a curvature histogram from a digitized 3D surface using a real object. Moreover, we propose to use the curvature histogram analysis for many steps of a reverse engineering process, which can be used to retrieve a CAD model from a digitized one for example. Our objective is to design a fast and fully automated method, which is seldom seen in reverse engineering. Experimental results applied on digitized 3D meshes show the efficiency and the robustness of our proposed method.

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Extraction of Reliable Primitives from Unorganized Point Clouds

Cited by 22 publications

References 19 publications

Denoising and Inpainting for Point Clouds Compressed by V-PCC

Denoising and Inpainting for Point Clouds Compressed by V-PCC

Geometric Primitives in LiDAR Point Clouds: A Review

Analysis of digitized 3D mesh curvature histograms for reverse engineering

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