Planar Surface Detection via Vector Clustering
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Solution Overview
Problem
Conventional 3D scene modeling techniques are computationally intensive and require preconditions such as landmark scene points, which limits their efficiency and practicality in detecting planar surfaces for scene modeling.
Innovation Solution
An image-processing method and system that determines 3D coordinates and computes vectors for scene points, clusters them based on orientation and distance information, and detects planar surfaces without relying on landmark points, using a combination of sensors and processing circuits to analyze and segment the scene into distinct planar surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional polygon mesh techniques are used to generate 3D models from all pixel data, then the realism and quality of 3D models is improved, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the scene into distinct planar surfaces by clustering scene points based on orientation and distance. Instead of processing all pixel data uniformly, the method divides the scene into meaningful planar regions (walls, floors, ceilings) and models only these surfaces, dramatically reducing computational complexity while maintaining 3D model quality.
Solution Approach 2:
The patent extracts only the essential geometric features (planar surfaces) from the complete pixel data. By identifying and extracting planar regions through vector computation and clustering, the method eliminates redundant processing of non-planar or irrelevant pixels, reducing computational burden while preserving the key structural elements needed for realistic 3D modeling.
2Measurement precision
If conventional techniques process all pixel data to identify landmark scene points, then depth information can be obtained, but the processing time and computational resources are excessively consumed
Solution Approach 1:
The patent extracts depth information directly from sensors without requiring the time-consuming process of identifying landmark scene points from all pixel data. The method computes vectors and clusters scene points based on orientation and distance, extracting the necessary depth information efficiently while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary clustering of scene points into planar surfaces before detailed 3D modeling. By pre-organizing points into planar regions based on orientation and distance, the method prepares the data structure in advance, eliminating the need for repeated processing during model generation and significantly reducing overall processing time.
3Reliability
If conventional methods require landmark scene points as a precondition, then 3D reconstruction can be achieved, but the method becomes less versatile and cannot be applied to scenes without identifiable landmarks
Solution Approach 1:
The patent makes the system self-sufficient by eliminating the external precondition of landmark scene points. The method uses sensor data and intrinsic geometric relationships (orientation and distance of scene points) to automatically identify planar surfaces and perform 3D reconstruction. This self-service approach allows the system to work reliably in any scene without requiring pre-existing landmarks or manual intervention.
Solution Approach 2:
The patent creates a universal method that can be applied to any scene type regardless of the presence of landmarks. By using planar surface detection based on orientation clustering and distance analysis, the technique works equally well for scenes with landmarks, without landmarks, or with varying scene compositions, greatly enhancing adaptability and versatility.
Data Source
AI summary
An image-processing method and system are provided for detection of planar surfaces for use in scene modeling of a captured scene, which includes determination of three dimensional (3D) coordinates that corresponds to a plurality of scene points of the captured scene. The 3D coordinates are determined based on depth information associated with the plurality of scene points. A plurality of vectors are computed for the plurality of scene points, based on the determined 3D coordinates. The computed plurality of vectors associated with the captured scene are clustered into a first set of clusters based on orientation information associated with each of the computed plurality of vectors. Further, for each cluster of the first set of clusters, a second set of clusters, are determined based on distance information associated with the computed plurality of vectors, followed by detection of a plurality of planar surfaces in the captured scene.