Point Cloud Encoding via Color-Based Clustering
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Solution Overview
Problem
The existing methods for encoding point clouds representing three-dimensional objects face inefficiencies due to the increase in data amount as the number of points increases, particularly because position and color information are indicated for each point.
Innovation Solution
An information processing device and method that perform clustering processing to classify points in a point cloud by color, converting the normal structure to a cluster structure where position information is classified into clusters and color information is indicated for each cluster, followed by encoding the point cloud in this cluster structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If position information and color information are indicated for each point in the point cloud, then the point cloud can represent the three-dimensional object with complete data, but the data amount increases as the number of points increases
Solution Approach 1:
The patent segments the point cloud into clusters based on spatial proximity and color similarity. Instead of storing complete position and color information for each individual point, the system groups points into clusters and stores representative information for each cluster, thereby reducing the overall data amount while maintaining the essential characteristics of the three-dimensional object.
Solution Approach 2:
The patent merges multiple points into single clusters, combining their position and color information into a unified cluster representation. This merging process reduces the number of individual data entries while preserving the collective information needed to represent the three-dimensional object, directly addressing the data amount issue.
2Measurement precision
If the number of points in the point cloud increases to improve representation accuracy, then the three-dimensional object can be represented more accurately, but the data amount increases leading to reduced encoding efficiency
Solution Approach 1:
The patent applies segmentation by dividing the point cloud into meaningful clusters that represent distinct regions of the three-dimensional object. This allows the system to maintain representation accuracy through semantic grouping while reducing the number of individual points that need to be encoded, thereby improving encoding efficiency.
Solution Approach 2:
The patent changes the representation parameters from individual point-level data to cluster-level data. By transforming the data structure from a list of individual points to a list of clusters with representative properties, the system maintains the essential information for accurate representation while significantly reducing the data volume that needs to be encoded.
Data Source
AI summary
There is provided an information processing device and method capable of improving encoding efficiency. Clustering processing of classifying each point of a point cloud representing a three-dimensional object as a set of points into a cluster for each color is performed to convert the point cloud having a normal structure, which is a data structure in which position information and color information of each point of the point cloud are indicated for each point, into the point cloud having a cluster structure, which is a data structure in which the position information of each point of the point cloud is classified into the cluster and the color information of each point is indicated for each cluster; and the point cloud of the cluster structure generated by the clustering processing is encoded. The present disclosure can be applied to, for example, an information processing device, an electronic device, an information processing method, an information processing system, a program, or the like.