RGB-D Point Cloud Registration Based on Salient Object Detection

Wan, Teng; Du, Shaoyi; Cui, Wenting; Yao, Runzhao; Ge, Yuyan; Li, Ce; Gao, Yue; Zheng, Nanning

doi:10.1109/tnnls.2021.3053274

Cited by 26 publications

(12 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to [7], Zhou et al [23] have utilized the FPFH descriptor to match points efficiently. In order to register RGB-D point clouds, the authors of [9,14] have applied texture information for extracting correspondences. Though the above methods can extract correspondences effectively, they are easily disturbed by large noise.…”

Section: Related Workmentioning

confidence: 99%

“…As an ingredient for point cloud registration, correspondence point extraction aims at estimating exactly the matching points between a pair of point clouds. In recent years, researchers have developed many remarkable methods to extract correspondence points [7,9,14,16,17]. Nevertheless, since existing methods usually only utilize the ge-ometry information of the input clouds, they have ambiguity problems when handling point clouds containing noise or weak texture regions.…”

Section: Correspondence Extractionmentioning

confidence: 99%

“…To extract correspondences accurately, researchers first introduced the methods based on point features including the fast point feature histograms (abbreviated as FPFH) feature [7] and integral volume descriptor [8]. Then, to enhance robustness of the above methods, more approaches were proposed by using high level information, such as color information [9][10][11][12][13][14], planar structure [15,16], and hybrid structure [17], etc. For point clouds alignment, many remarkable methods have been presented in terms of efficiency and robustness.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information

Guo

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Rigid registration of 3D indoor scenes is a fundamental yet vital task in various fields that include remote sensing (e.g., 3D reconstruction of indoor scenes), photogrammetry measurement, geometry modeling, etc. Nevertheless, state-of-the-art registration approaches still have defects when dealing with low-quality indoor scene point clouds derived from consumer-grade RGB-D sensors. The major challenge is accurately extracting correspondences between a pair of low-quality point clouds when they contain considerable noise, outliers, or weak texture features. To solve the problem, we present a point cloud registration framework in view of RGB-D information. First, we propose a point normal filter for effectively removing noise and simultaneously maintaining sharp geometric features and smooth transition regions. Second, we design a correspondence extraction scheme based on a novel descriptor encoding textural and geometry information, which can robustly establish dense correspondences between a pair of low-quality point clouds. Finally, we propose a point-to-plane registration technology via a nonconvex regularizer, which can further diminish the influence of those false correspondences and produce an exact rigid transformation between a pair of point clouds. Compared to existing state-of-the-art techniques, intensive experimental results demonstrate that our registration framework is excellent visually and numerically, especially for dealing with low-quality indoor scenes.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Correspondence Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information

Guo

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Currently, learning-based registration techniques are very popular, for example, a global registration method based on deep-learning has been developed in [14], and a deep outlier elimination method for the correspondence determination/rejection stage of registration via spatial consistency has also been presented in [15]. In addition, Huang et al tried to solve the point cloud registration problem with low overlap ratio using learning techniques [16], and RGB color and depth information was also considered in the feature extraction process of registration by Wan et al [17]. However, the main drawbacks of such approaches are the need for large training sets, unexpected performance loss in unknown scenes, and no guarantees of similar performance outside the training area [13].…”

Section: Introductionmentioning

confidence: 99%

“…Fig 17. Registration results of 2D shapes for varying outlier levels: the number of outliers is 10%, 20%, 30%, 50%, and 70% of the number of points in the original model set-(i) the shapes before registration and (ii) registration results of AfICP-PL-corr (our algorithm)…”

mentioning

confidence: 99%

Robust affine registration method using line/surface normals and correntropy criterion

Yïlmaz

Temeltaş

2022

Complex Intell. Syst.

View full text Add to dashboard Cite

The problem of matching point clouds is an efficient way of registration, which is significant for many research fields including computer vision, machine learning, and robotics. There may be linear or non-linear transformation between point clouds, but determining the affine relation is more challenging among linear cases. Various methods have been presented to overcome this problem in the literature and one of them is the affine variant of the iterative closest point (ICP) algorithm. However, traditional affine ICP variants are highly sensitive to effects such as noises, deformations, and outliers; the least-square metric is substituted with the correntropy criterion to increase the robustness of ICPs to such effects. Correntropy-based robust affine ICPs available in the literature use point-to-point metric to estimate transformation between point clouds. Conversely, in this study, a line/surface normal that examines point-to-curve or point-to-plane distances is employed together with the correntropy criterion for affine point cloud registration problems. First, the maximum correntropy criterion measure is built for line/surface normal conditions. Then, the closed-form solution that maximizes the similarity between point sets is achieved for 2D registration and extended for 3D registration. Finally, the application procedure of the developed robust affine ICP method is given and its registration performance is examined through extensive experiments on 2D and 3D point sets. The results achieved highlight that our method can align point clouds more robustly and precisely than the state-of-the-art methods in the literature, while the registration time of the process remains at reasonable levels.

show abstract

MVSalNet: Multi-view Augmentation for RGB-D Salient Object Detection

Zhou

Wang

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

RGB-D Point Cloud Registration Based on Salient Object Detection

Cited by 26 publications

References 45 publications

A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information

A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information

Robust affine registration method using line/surface normals and correntropy criterion

MVSalNet: Multi-view Augmentation for RGB-D Salient Object Detection

Contact Info

Product

Resources

About