3D Scene Modeling with Curvature-Based Point-Cloud Segmentation
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Solution Overview
Problem
Existing methods struggle to reliably segment complex scenes with many objects and objects of complex shapes from three-dimensional point clouds, often leading to over-segmentation or under-segmentation, making it difficult to derive accurate three-dimensional models for tasks like CAD, CAE, and CAM.
Innovation Solution
A method involving curvature-based and region-growing segmentations, combined with deselection criteria, is used to segment point clouds into clusters representing objects. This includes generating parametric surfaces, determining local principal curvature, and using region-growing methods to form clusters, with deselection of less relevant parts based on curvature and spatial properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If curvature-based segmentation is used to segment point clouds, then segmentation can be performed based on surface characteristics, but over-segmentation occurs when many different surface curvatures are present in the scene
Solution Approach 1:
The patent applies multi-stage segmentation by first performing curvature-based segmentation to divide the point cloud into groups based on surface curvature characteristics, then performing region-growing segmentation on selected groups to form clusters, and finally performing a second region-growing segmentation on the remaining point cloud. This hierarchical segmentation approach prevents over-segmentation by combining multiple segmentation strategies.
Solution Approach 2:
The patent applies different segmentation strategies to different regions of the point cloud based on their local characteristics. Curvature-based segmentation is applied to identify regions with specific surface properties, while region-growing methods are applied to other regions. This allows the segmentation process to adapt to local variations in surface curvature and object geometry.
2Ease of operation
If region-growing segmentation is used to segment point clouds, then objects can be clustered based on spatial continuity, but under-segmentation occurs when clusters comprise several objects or more parts than intended
Solution Approach 1:
The patent performs region-growing segmentation in multiple stages: first on selected groups obtained from curvature-based segmentation, then on the remaining point cloud. By dividing the region-growing process into separate stages applied to different subsets of the point cloud, the method prevents under-segmentation while maintaining automatic operation.
Solution Approach 2:
The patent performs preliminary curvature-based segmentation to identify and select specific groups of points before applying region-growing segmentation. This preliminary action prepares the data by pre-identifying regions that should be processed separately, thereby preventing under-segmentation in subsequent region-growing steps.
3Reliability
If multiple segmentation methods are combined to improve segmentation accuracy, then object identification becomes more reliable, but the complexity of the segmentation process increases
Solution Approach 1:
The patent divides the complex segmentation process into distinct, sequential stages: curvature-based segmentation, selection of groups, region-growing segmentation on selected groups, deselection of certain clusters, and region-growing segmentation on remaining points. This structured multi-stage approach manages complexity by breaking down the overall task into manageable steps with clear inputs and outputs at each stage.
Solution Approach 2:
The patent selectively discards certain first clusters based on predefined criteria before performing the second region-growing segmentation. This discarding step simplifies the remaining point cloud by removing already-processed or irrelevant regions, thereby reducing the complexity of subsequent segmentation operations while maintaining overall reliability.
Data Source
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AI summary
A method for deriving a three-dimensional model of a scene (1) is provided, wherein the scene comprises at least one object (01-05). The method comprises the following steps: a) performing a physical measurement on the scene (1), which leads to a three-dimensional point cloud (PC), b) generating at least one surface (S), in particular a parametric surface, from points of the point cloud (PC) and determining a respective local principal curvature (k) for the individual points of the point cloud (PC), c) performing a first curvature-based segmentation of the point cloud (PC) into at least two groups of points (g1, g2) based on at least one threshold value (t) of the local principal curvature (k), d) performing a first region-based segmentation of at least one selected group (g1) of the groups of points (g1, g2) obtained in step c), said segmentation being based on a region-growing method and leading to a plurality of first clusters (c1a, c1b), e) determining a remaining point cloud (RPC) by deselecting a sub-set of these first clusters (c1b) based on a pre-defined deselection criterion and discarding this sub-set (c1b) from the point cloud (PC) at least for the following step f), f) determining a plurality of second clusters (c2a, c2b) by performing a second region-based segmentation of the remaining point cloud (RPC), said segmentation being based on a region-growing method. Furthermore, a corresponding computer program product for carrying out such a method is provided.