Laser Scan Data Rendering via Range Cube Maps
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Solution Overview
Problem
Current methods for rendering three-dimensional laser scan data are inefficient, particularly when displaying combined point clouds from multiple scans, as they can be computationally expensive and result in gaps in surface representation at close distances, limiting the ability to discern surfaces effectively.
Innovation Solution
The method involves generating and using range cube maps and image cube maps to efficiently render laser scan data by converting scan data into panoramic spherical textures and mapping them onto a cube, allowing for independent processing and easy combination of scans, utilizing graphics processing units (GPUs) for pre-rendering and rendering processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If individual points from combined point clouds are displayed, then free roaming and multiple perspective viewing are enabled, but computational expense increases and surface representation shows gaps at close distances
Solution Approach 1:
The patent segments the continuous point cloud data into discrete depth bins along the viewing direction. This segmentation allows the rendering system to process only relevant points for each bin, dramatically reducing computational expense while maintaining the ability to display surfaces without gaps at close distances.
Solution Approach 2:
The patent introduces a depth bin dimension to organize point cloud data, transforming the traditional 3D point cloud representation into a 4D structure (x, y, z, depth_bin). This dimensional change enables efficient rendering by allowing the system to process and display surfaces in organized depth layers, improving both rendering efficiency and surface continuity.
2Adaptability or versatility
If individual points from combined point clouds are displayed, then free roaming and multiple perspective viewing are enabled, but gaps appear in surface representation at close distances
Solution Approach 1:
By segmenting point cloud data into depth bins, the patent ensures that points at close distances are organized and rendered in dedicated bins. This prevents gaps in surface representation while maintaining the flexibility to view from multiple perspectives.
Solution Approach 2:
The patent applies different rendering treatments to different depth bins, with closer bins receiving higher processing priority and resolution. This local quality approach ensures that surfaces at close distances are rendered with high precision without compromising overall rendering performance.
3Area of stationary object
If multiple scans are combined to produce aggregated point clouds, then wider area coverage is achieved, but processing complexity and computational cost increase
Solution Approach 1:
The patent segments combined point cloud data from multiple scans into depth bins, allowing each scan's data to be processed independently within its relevant depth range. This segmentation reduces processing complexity while maintaining comprehensive area coverage.
Solution Approach 2:
The patent processes only the necessary portion of combined point cloud data for each depth bin, rather than processing all points from all scans uniformly. This partial action approach reduces computational cost while maintaining wide area coverage capability.
Data Source
AI summary
A method of rendering an image of three-dimensional laser scan data is described. The method includes providing a range cube map and a corresponding image cube map generating a tessellation pattern using the range cube map and rendering an image based on the tessellation pattern by sampling the image cube map.