Colored Point Cloud Registration by Depth Filtering

Choi, Ouk; Hwang, Wonjun

doi:10.3390/s21217023

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…Many state-ofthe-art deep learning registration methods rely solely on geometry information, neglecting texture information. However, some exceptions exist where these methods rely on intermediate media such as RGBD images, projection images, or depth maps [60][61][62]. Since deep learning methods typically process only relative positions of points, they lack colour information, which limits their applications.…”

Section: Fig 1 Visualisation Of the Point Clouds Registration Processmentioning

confidence: 99%

Fast adaptive multimodal feature registration (FAMFR): an effective high-resolution point clouds registration workflow for cultural heritage interiors

Foryś,

Sitnik,

Markiewicz

et al. 2023

Herit Sci

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Accurate registration of 3D scans is crucial in creating precise and detailed 3D models for various applications in cultural heritage. The dataset used in this study comprised numerous point clouds collected from different rooms in the Museum of King Jan III’s Palace in Warsaw using a structured light scanner. Point clouds from three relatively small rooms at Wilanow Palace: The King’s Chinese Cabinet, The King’s Wardrobe, and The Queen’s Antecabinet exhibit intricate geometric and decorative surfaces with diverse colour and reflective properties. As a result, creating a high-resolution full 3D model require a complex and time-consuming registration process. This process often consists of several steps: data preparation, registering point clouds, final relaxation, and evaluation of the resulting model. Registering two-point clouds is the most fundamental part of this process; therefore, an effective registration workflow capable of precisely registering two-point clouds representing various cultural heritage interiors is proposed in this paper. Fast Adaptive Multimodal Feature Registration (FAMFR) workflow is based on two different handcrafted features, utilising the colour and shape of the object to accurately register point clouds with extensive surface geometry details or geometrically deficient but with rich colour decorations. Furthermore, this work emphasises the challenges associated with high-resolution point clouds registration, providing an overview of various registration techniques ranging from feature-based classic approaches to new ones based on deep learning. A comparison shows that the algorithm explicitly created for this data achieved much better results than traditional feature-based or deep learning methods by at least 35%.

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Section: Fig 1 Visualisation Of the Point Clouds Registration Processmentioning

confidence: 99%

Fast adaptive multimodal feature registration (FAMFR): an effective high-resolution point clouds registration workflow for cultural heritage interiors

Foryś,

Sitnik,

Markiewicz

et al. 2023

Herit Sci

View full text Add to dashboard Cite

show abstract

“…Generally, the errors in color data registration with point cloud data stem from two factors: calculation errors in point clouds and registration errors between color images and structured-light images. To improve the registration accuracy of point clouds and color information, many algorithms have been developed, such as genetic algorithm-based algorithms [37], four initial point pairs (FIPP) algorithms [38], and depth filtering algorithms [39]. However, regardless of how good a registration algorithm is, there will always be errors when performing registration between color images and structured-light images.…”

Section: Obtain Color Point Cloudsmentioning

confidence: 99%

A method for high dynamic range 3D color modeling of objects through a color camera

Zhang

Qiao

Xia³

et al. 2022

Machine Vision and Applications

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This paper proposes a novel method for enhancing the dynamic range of structured-light cameras to solve the problem of highlight that occurs when 3D modeling highly reflective objects using the structured-light method. Our method uses the differences in quantum efficiency between R, G, and B pixels in the color image sensor of a monochromatic laser to obtain structured-light images of an object under test with different luminance values. Our approach sacrifices the resolution of the image sensor to increase the dynamic range of the vision system. Additionally, to enhance our system, we leverage the backgrounds of structured-light stripe pattern images to restore the color information of measured objects, whereas the background is often removed as noise in other 3D reconstruction systems. This reduces the number of cameras required for 3D reconstruction and the matching error between point clouds and color data. We modeled both highly reflective and non-highly reflective objects and achieved satisfactory results.

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Rotation axis calibration of a 3D scanning system based on dual-turntable angle cancellation

Song

Liu

et al. 2023

Appl. Opt.

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Rotation axis calibration is crucial for high-precision automatic point cloud stitching in turntable-based 3D scanning systems. To achieve a 360° sampling with a 2D calibrator in rotation axis calibration, this paper proposes a dual-turntable angle cancellation (DTAC) method. DTAC introduces an auxiliary turntable to keep a proper relative angle between the 3D sensor and the calibrator during the calibration process. The auxiliary turntable rotates at the same and opposite angle as the main turntable and cancels the increment of the relative angle. By projecting the feature points on the planar calibrator from real-world space to virtual calibration space, the projected points all share the same rotation axis of the main turntable. Further, a layered circle center extraction (LCCE) algorithm is applied to deal with outlier data points. The algorithm uses a two-step robust estimation strategy combining RANSAC circle fitting with a median noise filter for circle center selection. The standard ball reconstruction experiment shows that the 3D system calibrated by the method achieves a mean absolute error of 0.022 mm and root mean square error of 0.025 mm within the measurement distance of 60–70 cm. Point cloud stitching experiments of different types of objects show that our method outperforms other state-of-the-art methods in stitching accuracy. The DTAC method and LCCE algorithm can improve turntable-based 3D scanning systems.

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Colored Point Cloud Registration by Depth Filtering

Cited by 6 publications

References 40 publications

Fast adaptive multimodal feature registration (FAMFR): an effective high-resolution point clouds registration workflow for cultural heritage interiors

Fast adaptive multimodal feature registration (FAMFR): an effective high-resolution point clouds registration workflow for cultural heritage interiors

A method for high dynamic range 3D color modeling of objects through a color camera

Rotation axis calibration of a 3D scanning system based on dual-turntable angle cancellation

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