3D Point Cloud Generation from Composed Image and Depth Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for acquiring three-dimensional information require a large number of cameras and significant hardware resources, making it difficult to capture dynamic shapes, especially in a studio setting, and are inefficient for real-time three-dimensional shape acquisition.
Innovation Solution
A three-dimensional information generating device and method that uses a single camera to acquire image and distance information, composing image data with supplementary pixel information to generate point cloud data, allowing for efficient three-dimensional shape reconstruction without the need for multiple cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a larger number of cameras are used to acquire three-dimensional information with higher fidelity, then the quality of three-dimensional information is improved, but hardware resources and processing time increase significantly
Solution Approach 1:
The patent segments the image data into multiple pieces and processes each piece independently to generate three-dimensional information. By dividing the image data acquisition and processing into smaller units, the system achieves high-fidelity three-dimensional reconstruction without requiring a large number of cameras, thus resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent introduces depth information as an additional dimension by acquiring distance information corresponding to each pixel in the image data. This transforms two-dimensional image data into three-dimensional point cloud data by adding the depth dimension, enabling high-fidelity three-dimensional reconstruction using a single camera rather than multiple cameras
2Measurement precision
If a larger number of cameras are used to acquire three-dimensional information with higher fidelity, then the quality of three-dimensional information is improved, but processing time increases
Solution Approach 1:
The patent segments image data into multiple pieces and processes each piece independently and in parallel. This segmentation approach reduces the overall processing time while maintaining high-fidelity three-dimensional information, as each segment can be processed separately and efficiently without requiring sequential processing of entire datasets from multiple cameras
Solution Approach 2:
The patent performs preliminary processing by generating depth information for each pixel and creating point cloud data before final composition. This preliminary action prepares the data in advance, reducing the time required for final three-dimensional reconstruction and enabling real-time or near-real-time processing
3Loss of information
If multiple cameras are used to acquire dynamic three-dimensional shape, then the completeness of three-dimensional information is improved, but it becomes difficult to acquire the three-dimensional shape in real-time
Solution Approach 1:
The patent adds the depth dimension to two-dimensional image data by acquiring distance information for each pixel. This dimensional transformation enables complete three-dimensional information acquisition from a single camera viewpoint, eliminating the need for multiple cameras while maintaining real-time processing capability
Solution Approach 2:
The patent uses depth information as an intermediary to bridge two-dimensional image data and three-dimensional reconstruction. By introducing this intermediate depth map, the system achieves complete three-dimensional information without requiring multiple camera viewpoints, thus enabling real-time processing of dynamic objects
4Device complexity
If a single camera is used to acquire three-dimensional information, then hardware resources are reduced, but the completeness of three-dimensional information from multiple viewpoints is lost
Solution Approach 1:
The patent compensates for the lack of multiple viewpoints by adding the depth dimension to single-camera image data. By transforming 2D image data into 3D point cloud data through depth information acquisition, the system achieves complete three-dimensional reconstruction from a single viewpoint, eliminating the need for multiple cameras while maintaining information completeness
Data Source
AI summary
A three-dimensional information generating device includes a first acquisition unit configured to acquire first image information including image information acquired by imaging a subject and distance information corresponding to this image information, a second acquisition unit configured to acquire second image information including at least pixel information, an image composing unit configured to generate a composed image by composing the image information included in the acquired first image information and information included in the second image information, and a point cloud data generating unit configured to generate point cloud data on the basis of the generated composed image and the distance information included in the first image information wherein the image composing unit generates an image in which pixel information of a column or a row in the image information included in the acquired first image information and the pixel information included in the second image information are alternately provided.