3D Point Cloud View Selection Using Normal Vector Orientation
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Solution Overview
Problem
Existing point cloud viewers often default to an object-centered perspective mode, which is unsuitable for indoor or outdoor scans, leading to a lack of useful information for users and difficulty in manual view adjustment without metadata, especially in formats like PLY or LAS.
Innovation Solution
A method and device that determine a view of a 3D point cloud for display on a 2D screen by using available or estimated normal vectors to automatically select an interior or exterior view based on the orientation of the scanning device, employing threshold comparisons and symmetry analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If object-centered perspective mode is used by default, then the rendered 3D point cloud can be entirely visualized in a central area of the screen, but useful information inside the scanned space is hidden and not visible to the user
Solution Approach 1:
The patent applies preliminary action by estimating normal vectors for all points before view determination, and by pre-calculating the distribution characteristics of these normal vectors to predict the most suitable view mode. This allows the system to automatically select between interior and exterior views based on the scanned space characteristics without requiring manual user input during the viewing process.
2Ease of operation
If manual view adjustment is required without metadata, then users can potentially select appropriate views, but the process becomes difficult and time-consuming
Solution Approach 1:
The patent implements self-service by enabling the point cloud viewing system to automatically determine the appropriate view mode (interior or exterior) based on the distribution characteristics of normal vectors. The system serves itself by autonomously analyzing the geometric properties of the point cloud data and selecting the most suitable visualization perspective without requiring manual user intervention or metadata input.
3Extent of automation
If normal vectors are estimated for all points, then the view can be automatically determined based on normal vector distribution, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by estimating normal vectors for all points (excessive action) to ensure complete coverage of the point cloud data, which enables robust automatic view determination. This comprehensive approach ensures that even if some points have inaccurate normal vectors, the overall distribution characteristics remain reliable for view selection.
Solution Approach 2:
The patent utilizes parameter changes by analyzing the distribution characteristics of normal vectors (such as the concentration of normal vectors pointing in similar directions) to dynamically determine the appropriate view mode. The system changes the viewing parameters based on the estimated normal vector distribution, switching between interior and exterior views as needed.
Data Source
AI summary
A method is disclosed for determining a view of a three-dimensional, 3D, point cloud for display on a two-dimensional, 2D, screen. The method is performed by a computing device. The method includes obtaining the 3D point cloud. If a normal vector is available for each point of the 3D point cloud, the method includes determining the view based on directions of normal vectors for points of the 3D point cloud. Otherwise the method includes estimating a normal vector for each point of the 3D point cloud; and determining the view based on estimated normal vectors with symmetrical directions.