Point Cloud Cluster Spatial Relationship Vectorization
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Solution Overview
Problem
Current systems for determining spatial relationships between point cloud clusters in three-dimensional environments are limited in accurately characterizing and representing the spatial arrangements of objects, particularly in mixed reality and augmented reality applications, where precise spatial information is crucial for simulating physics and interactions.
Innovation Solution
A method involving a device with processors and memory that generates a point cloud by capturing images from different perspectives, spatially disambiguates points into clusters, and determines spatial relationship vectors based on the volumetric arrangement of these clusters, which are then added to characterization vectors for each point, enabling precise characterization of spatial relationships between clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial relationships between point cloud clusters are determined using traditional methods, then the system can represent basic three-dimensional space, but the accuracy and precision of spatial arrangement characterization is insufficient for mixed reality and augmented reality applications
Solution Approach 1:
The patent segments the point cloud into multiple clusters, where each cluster represents a distinct object or group of points. By dividing the point cloud into manageable clusters and computing spatial relationships between them, the system achieves precise spatial characterization without overwhelming computational complexity. Each cluster's centroid and volumetric properties are calculated independently, enabling efficient spatial relationship determination.
Solution Approach 2:
The patent transitions from representing points only by their three-dimensional coordinates to including spatial relationship vectors that capture relationships between clusters. This adds a new dimensional layer of information - the spatial relationship dimension - which characterizes how clusters are arranged relative to each other in terms of distance, orientation, and volumetric arrangement, thereby improving measurement precision.
2Measurement precision
If detailed spatial relationship vectors are computed for all point cloud clusters, then precise spatial information is obtained, but computational resources and processing time increase
Solution Approach 1:
By segmenting the point cloud into clusters first, the system reduces the computational burden compared to processing individual points. The spatial relationship vectors are computed between cluster centroids rather than between all pairs of points, significantly reducing the number of computations required while maintaining precision at the cluster level.
Solution Approach 2:
The patent computes spatial relationship vectors selectively - only between identified clusters rather than all possible point pairs. This partial action approach focuses computational resources on the most significant spatial relationships (between distinct objects/clusters) while ignoring redundant computations within homogeneous point groups, thereby reducing processing time while preserving essential spatial information.
Data Source
AI summary
In one implementation, a method of determining spatial relationships of point cloud clusters is performed at a device including one or more processors and non-transitory memory. The method includes obtaining a point cloud including a plurality of points, wherein the plurality of points includes a first cluster of points and a second cluster of points, wherein a particular point of the first cluster of points is associated with a characterization vector that includes a set of coordinates of the particular point in a three-dimensional space and a cluster identifier of the first cluster of points. The method includes determining a spatial relationship vector based on the volumetric arrangement of the first cluster of points and the second cluster of points, wherein the spatial relationship vector characterizes the spatial relationship between the first cluster of points and the second cluster of points. The method includes adding the spatial relationship vector to characterization vector.