Mesh Resolution Segmentation for Point Cloud Data Capacity
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Solution Overview
Problem
Existing systems face challenges in balancing the capacity and accuracy of image data generated from point cloud data, particularly when reducing the accuracy of object shapes in LiDAR-based systems, leading to difficulties in recognizing object shapes effectively.
Innovation Solution
An information processing apparatus and method that identifies object shapes in point cloud data, generates first and second mesh data using different mesh resolutions, and combines these to create three-dimensional data, allowing for flexible resolution management and display to balance data capacity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high mesh resolution is used to accurately represent object shapes in point cloud data, then shape accuracy is improved, but data capacity increases significantly
Solution Approach 1:
The patent segments the scene into multiple objects and applies different mesh resolutions to different objects based on their importance or characteristics. This allows high resolution for critical objects while using lower resolution for less important ones, thereby maintaining shape accuracy where needed while controlling overall data capacity.
Solution Approach 2:
The patent implements local quality by assigning different mesh resolutions to different spatial regions or objects within the point cloud data. Critical areas receive high-resolution mesh representation while other areas use lower resolution, optimizing the balance between shape accuracy and data capacity locally rather than uniformly across the entire dataset.
2Quantity of substance
If low mesh resolution is used to reduce data capacity, then data capacity is reduced, but shape recognition accuracy deteriorates
Solution Approach 1:
By segmenting objects and identifying their relative importance, the system can apply low mesh resolution to non-critical objects while maintaining high resolution for objects that require accurate shape recognition. This segmentation approach ensures that shape recognition accuracy is preserved for important objects even when overall data capacity is reduced.
Solution Approach 2:
The patent dynamically changes the mesh resolution parameter based on object characteristics, distance from camera, or importance level. This parameter adaptation allows the system to maintain adequate shape recognition accuracy by adjusting resolution locally rather than using a fixed low resolution across all objects.
Data Source
AI summary
An object of the present invention is to provide an information processing apparatus being capable of suppressing an increase in capacity and preventing a decrease in accuracy of a shape of an object when generating image data based on point cloud data. The information processing apparatus according to the present disclosure includes: an identification unit that identifies a shape of an object included in point cloud data; a generation unit that generates first mesh data of a first object whose shape is identified, and generates second mesh data of a second object whose shape is identified, by using a mesh resolution different from a mesh resolution used in generating the first mesh data; and an integration unit that generates three-dimensional data acquired by combining the first mesh data and the second mesh data.