Variable Density Depthmap for Navigation Data Optimization
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Solution Overview
Problem
Conventional depthmaps require significant storage, processing power, and bandwidth due to their constant density, which is inefficient for navigation systems that need to display detailed foreground and background information effectively.
Innovation Solution
The implementation of variable density depthmaps, where cells are sized based on distance from the user's location, allowing for higher density near the center and lower density further away, reducing the amount of data needed for storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If constant density depthmaps are used, then accurate placement of objects is maintained, but storage requirements and processing power increase significantly
Solution Approach 1:
The patent applies local quality by using variable cell sizes in the depthmap where cells closer to the user have smaller dimensions (higher density) for accurate object placement, while cells farther away have larger dimensions (lower density) to reduce data storage. This creates a spatially varying resolution that matches the visual importance of different regions.
Solution Approach 2:
The depthmap is segmented into multiple cells with different sizes based on their distance from the user. This segmentation allows the system to treat different spatial regions differently, using fine-grained cells near the user and coarse-grained cells in the distance, thereby reducing overall data requirements while maintaining necessary precision where needed.
2Loss of information
If constant density depthmaps are used, then complete spatial information is retained, but bandwidth and transmission time increase
Solution Approach 1:
The variable cell size approach applies local quality by transmitting detailed spatial information only in regions where it matters (near the user with small cells) while using coarser representations in distant regions. This reduces the total data volume for transmission while preserving spatial information completeness in visually critical areas.
3Measurement precision
If higher density cells are used near user location, then object placement accuracy is improved, but processing power requirements increase
Solution Approach 1:
The patent implements local quality by concentrating processing resources on small, high-density cells near the user where accurate object placement is critical, while using larger, low-density cells in the background that require less processing. This spatially differentiated approach reduces overall processing power requirements compared to uniform high-density maps.
4Productivity
If variable density cells are used, then data transmission efficiency is improved, but device complexity increases
Solution Approach 1:
The variable density depthmap is segmented into cells of different sizes organized by distance from the user. This segmentation provides a systematic structure that, while more complex than uniform grids, follows clear geometric rules that simplify implementation and management compared to arbitrary variable-density approaches.
Data Source
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AI summary
One or more systems, devices, and/or methods for organizing spatial data are disclosed. For example, a method includes receiving a location data point from a navigation device. A controller compares the distance from each of a plurality of data points to the location data point. In constructing a depthmap that associates each of the data points with the distance from the location data point, the controller assign data points within a minimum distance from the location data point into a first cell and data points greater than the minimum distance from the location data point into a second cell. The second cell is larger than the first cell and corresponds to a larger geographic area than the first cell. The depthmap may be configured to position points of interest in a panoramic or street level view image.