MaskNet++: Inlier/outlier identification for two point clouds

Zhou, Ruqin; Wang, Hanyun; Li, Xixing; Guo, Yulan; Dai, Chenguang; Jiang, Wanshou

doi:10.1016/j.cag.2022.01.008

Cited by 10 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A standard RF classifier was used in two approaches, non-semantic and semantic (i.e., with a classifier trained separately per each semantic class), and applied to large datasets of outdoor scenes [ 33 ]. Removal of outliers in a pair of point clouds of the same scene acquired from two viewpoints was demonstrated in [ 34 ].…”

Section: Related Work On Filtering 3d Point Cloudsmentioning

confidence: 99%

Filtering Organized 3D Point Clouds for Bin Picking Applications

Franaszek,

Rachakonda,

Saidi

2024

Applied Sciences

View full text Add to dashboard Cite

In robotic bin-picking applications, autonomous robot action is guided by a perception system integrated with the robot. Unfortunately, many perception systems output data contaminated by spurious points that have no correspondence to the real physical objects. Such spurious points in 3D data are the outliers that may spoil obstacle avoidance planning executed by the robot controller and impede the segmentation of individual parts in the bin. Thus, they need to be removed. Many outlier removal procedures have been proposed that work very well on unorganized 3D point clouds acquired for different, mostly outdoor, scenarios, but these usually do not transfer well to the manufacturing domain. This paper presents a new filtering technique specifically designed to deal with the organized 3D point cloud acquired from a cluttered scene, which is typical for a bin-picking task. The new procedure was tested on six different datasets (bins filled with different parts) and its performance was compared with the generic statistical outlier removal procedure. The new method outperforms the general procedure in terms of filtering efficacy, especially on datasets heavily contaminated by numerous outliers.

show abstract

Section: Related Work On Filtering 3d Point Cloudsmentioning

confidence: 99%

Filtering Organized 3D Point Clouds for Bin Picking Applications

Franaszek,

Rachakonda,

Saidi

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In addition, a projector is used to discriminate a small number of outlier points based on point cloud consistency 5 . A non-local self-similarity-based point cloud denoising method can preserve sharp feature information using adaptive curvature thresholding, propose a projection height vector perceptron, and introduce a weighted parametric optimization height matrix to achieve high-precision reconstruction 9 .The MaskNet++ framework for inner-outer point detection uses spatial self-attention and channel cross-attention mechanisms to identify outlier noise in point clouds, effectively removing isolated points and outlier points 10 . The proposed type-based outlier removal framework quantifies the input point cloud features by three parameters and divides the point cloud to reject outliers by the proposed single-criterion method 11 .…”

Section: Related Workmentioning

confidence: 99%

Research on point cloud hole filling and 3D reconstruction in reflective area

Sun,

Miao,

Wang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Abstract3D reconstruction is the process of obtaining the three-dimensional shape or surface structure of an object, which is widely used in advanced manufacturing fields such as automotive, aerospace, industrial inspection, and reverse engineering. However, due to the structural characteristics of the component itself, the reflective properties of the coating material, and other factors, there may be specular reflection during image acquisition, making it difficult to achieve complete 3D reconstruction of the component. This paper proposes a method to address the problem of incomplete 3D reconstruction of strongly reflective objects by recognizing outlier points and filling point cloud holes. The proposed View-Transform-PointNet outlier point recognition network improves the alignment of the initial point cloud plane and implements secondary alignment of the point cloud based on the perpendicularity between the outlier plane in mixed reflection and the point cloud plane. The point cloud hole-filling method is based on the principle of outlier formation and approximates a local Gaussian distribution to linear variation. The distance between the end of each outlier plane and the real surface is calculated to repair the depth information of outlier points. The proposed method achieves a 39.4% increase in the number of point cloud filling, a 45.2% increase in the number of triangular mesh faces, a 46.9% increase in surface area, and a chamfer distance (CD) of 0.4471009, which is better than existing geometric repair methods in terms of standard deviation and smoothness. The method improves the alignment of initial point cloud planes and enhances the accuracy of outlier point recognition, which are the main innovative points of this study. The 3D reconstruction of the repaired point cloud model is achieved through Poisson equation and parameter adjustment. The proposed method reduces the error caused by large curvature in the boundary region and improves the smoothness and accuracy of the reconstructed model.

show abstract

“…SANet [32] proposes a subtract attention module to aggregate the pointwise features and then obtain the local correspondence between each point to complete the partial point cloud registration. MaskNet++ [33] utilizes spatial selfattention and channel cross-attention mechanisms to extract pointwise features and exchange information, respectively. STORM [34] employs EdgeConv and Transformer [16] to map the input points to a feature space, then performs overlap prediction to identify common points, and Transformer to refine the features, finally completing registration.…”

Section: Related Workmentioning

confidence: 99%

A Partial‐to‐Partial Point Cloud Registration Method Based on Geometric Attention Network

Chen,

Wang,

et al. 2023

Journal of Sensors

View full text Add to dashboard Cite

Partial point cloud registration is an important step in generating a full 3D model. Many deep learning-based methods show good performance for the registration of complete point clouds but cannot deal with the registration of partial point clouds effectively. Recent methods that seek correspondences over downsampled superpoints show great potential in partial point cloud registration. Therefore, this paper proposes a partial-to-partial point cloud registration network based on geometric attention (GAP-Net), which mainly includes a backbone network optimized by a spatial attention module and an overlapping attention module guided by geometric information. The former aggregates the feature information of superpoints, and the latter focuses on superpoint matching in overlapping regions. The experimental results show that the method achieves better registration performance on ModelNet and ModelLoNet with lower overlap. The rotation error is reduced by 14.49% and 17.12%, respectively, which is robust to the overlap rate.

show abstract

MaskNet++: Inlier/outlier identification for two point clouds

Cited by 10 publications

References 32 publications

Filtering Organized 3D Point Clouds for Bin Picking Applications

Filtering Organized 3D Point Clouds for Bin Picking Applications

Research on point cloud hole filling and 3D reconstruction in reflective area

A Partial‐to‐Partial Point Cloud Registration Method Based on Geometric Attention Network

Contact Info

Product

Resources

About