A New Semantic Segmentation Method of Point Cloud Based on PointNet and VoxelNet

Zhou, Weihang; Lu, Jun‐Guo; Yue, Wen Long

doi:10.1109/ccdc.2019.8832574

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…Then, it continuously learns the local features of point-cloud data and, finally, the deep features. PointNet and PointNet++ have been applied to 3D object detection [15] and semantic segmentation [16] with good results. Wang et al [17] proposed the dynamic graph CNN (DGCNN) model, which has a similar network structure to PointNet.…”

Section: Model Matching and Transformingmentioning

confidence: 99%

Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping

Zhu

Dong

Shen

et al. 2023

Tsinghua Sci. Technol.

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The accuracy and efficiency of three-dimensional (3D) surface forming, which directly affects the cycle and quality of production, is important in manufacturing. In practice, given the uncertainty of metal plate springback, an error exists between the actual plate and the target surface, which creates a nonlinear mapping from computer aided design models to bending surfaces. Technicians need to reconfigure parameters and process a surface multiple times to delicately control springback, which greatly wastes human and material resources. This study aims to address the springback control problem to improve the efficiency and accuracy of sheet metal forming.A basic computation approach is proposed based on the DeepFit model to calculate the springback value in 3D surface bending. To address the sample data shortage problem, we put forward an advanced approach by combining a deep learning model with case-based reasoning (CBR). Next, a multi-model fused bending parameter generation framework is devised to implement the advanced springback computation approach through surface data preprocessing, CBR-based model matching, convolution neural network-based machining surface generation, and bending parameter generation with a series of model transformations. Moreover, the proposed approaches and the framework are verified by considering saddle surface processing as an example. Overall, this study provides a new idea to improve the accuracy and efficiency of surface processing.

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Section: Model Matching and Transformingmentioning

confidence: 99%

Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping

Zhu

Dong

Shen

et al. 2023

Tsinghua Sci. Technol.

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“…Various analytical, region-based and geometric methods (model fitting) are used for cluster analysis of point clouds, in particular for segmenting and extracting certain elements of a scene [GMR17, SWL + 16]. In addition, also more and more approaches based on machine learning are applied [ZLY19]. For a comparison of 3D data, Dobos et al [DFFW18] described a method that recognizes differences between 3D models in the screen space based on different data such as color, depth, normals and texture coordinates and visualizes them for the user.…”

Section: Introductionmentioning

confidence: 99%

Updating 3D Planning Data based on Detected Differences between Real and Planning Data of Building Interiors

Dietze¹,

Grimm²,

Jung³

2021

CSRN

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This paper presents a system to determine differences between 3D reconstructed interiors and their corresponding3D planning data with the aim of correcting identified differences and updating the 3D planning data based onthese deviations. Therefore, a point-based comparison algorithm was developed with which deviations can berecognized regardless of the topology of the data used. Usually, resolution and topology of a 3D reconstruction donot match the CAD data. Here, our solution overcomes this problem by segmenting and extracting objects relevantfor comparison (e.g., doors, windows) from the reconstruction and planning data separately with a subsequentanalysis of the proximity of these objects to connected walls within the corresponding data set. Starting from theconnection points of a segmented object to its walls, adjacent spatial data is located for a correction of detecteddifferences to update the 3D planning data. The quality of the result of the developed process is shown in differentexamples localizing doors and windows to find deviations. In addition, detected differences between the planningand the measurement data are visualized and compared with the ground truth state of the building interior.

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Section: Introductionmentioning

confidence: 99%

Updating 3D Planning Data based on Detected Differences between Real and Planning Data of Building Interiors[

Dietze¹,

Grimm²,

Jung³

2021

CSRN

View full text Add to dashboard Cite

This paper presents a system to determine differences between 3D reconstructed interiors and their corresponding 3D planning data with the aim of correcting identified differences and updating the 3D planning data based on these deviations. Therefore, a point-based comparison algorithm was developed with which deviations can be recognized regardless of the topology of the data used. Usually, resolution and topology of a 3D reconstruction do not match the CAD data. Here, our solution overcomes this problem by segmenting and extracting objects relevant for comparison (e.g., doors, windows) from the reconstruction and planning data separately with a subsequent analysis of the proximity of these objects to connected walls within the corresponding data set. Starting from the connection points of a segmented object to its walls, adjacent spatial data is located for a correction of detected differences to update the 3D planning data. The quality of the result of the developed process is shown in different examples localizing doors and windows to find deviations. In addition, detected differences between the planning and the measurement data are visualized and compared with the ground truth state of the building interior.

show abstract

A New Semantic Segmentation Method of Point Cloud Based on PointNet and VoxelNet

Cited by 8 publications

References 14 publications

Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping

Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping

Updating 3D Planning Data based on Detected Differences between Real and Planning Data of Building Interiors

Updating 3D Planning Data based on Detected Differences between Real and Planning Data of Building Interiors[

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