3D Point Cloud Generation from LIDAR and Optical Image Fusion
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Solution Overview
Problem
Current devices that capture range information and optical information for a scene, such as LIDAR and cameras, do not effectively combine these data types to create a comprehensive 3D representation, limiting the depth and accuracy of 3D imaging.
Innovation Solution
A method and system that combines range information from a ranging device, such as LIDAR, with optical images from a camera to produce a 3D point cloud, allowing for the creation of a 3D optical image by correlating range values with pixels in the optical image and using this data to generate a new optical image from a different perspective.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If range information from LIDAR and optical images from cameras are captured separately without effective combination, then each device can independently capture its respective data, but the depth perception and 3D representation accuracy are limited
Solution Approach 1:
The patent merges range information from LIDAR with optical images from cameras by correlating range values with pixel positions to generate a 3D point cloud. This combines data from multiple sensors into a unified 3D representation, improving depth perception accuracy while managing integration complexity through systematic processing steps.
Solution Approach 2:
The patent uses a 3D point cloud as an intermediary data structure to bridge range information and optical images. The point cloud serves as a common representation that integrates both data types, enabling accurate 3D reconstruction without direct complex interaction between the original data formats.
2Loss of information
If traditional stereo vision systems use two cameras spaced apart to create 3D images, then depth information can be obtained, but the system complexity and distortion issues increase
Solution Approach 1:
The patent combines monocular optical images with LIDAR range data to achieve 3D reconstruction, replacing the need for complex stereo camera systems. This merging of different data types (2D images with range information) provides complete depth information while simplifying the overall system configuration.
3Adaptability or versatility
If 3D images are created using red-blue anaglyph or polarized methods, then 3D effect can be achieved, but the viewing experience and image quality are compromised
Solution Approach 1:
The patent replaces traditional optical 3D display methods (anaglyph, polarized) with a computational approach that generates 3D point clouds and renders images from different perspectives. This substitution of mechanical/optical systems with computational processing preserves image quality while providing 3D viewing flexibility through software-based solutions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the creation of accurate and detailed 3D point clouds and images, enhancing the depth perception and representation of scenes by integrating range and optical data, which can be used for applications like stereo vision and 3D video production with reduced distortion.
Implementation Method 1
A LIDAR captures range information for a scene by emitting a flash of coherent light and measuring the amount of time it takes the coherent light to travel from the LIDAR to objects within the field of view of the LIDAR and back to the LIDAR
Data Source
AI summary
A method for combining range information with an optical image is provided. The method includes capturing a first optical image of a scene with an optical camera, wherein the first optical image comprising a plurality of pixels. Additionally, range information of the scene is captured with a ranging device. Range values are then determined for at least a portion of the plurality of pixels of the first optical image based on the range information. The range values and the optical image are combined to produce a 3-dimensional (3D) point cloud. A second optical image of the scene from a different perspective than the first optical image is produced based on the 3D point cloud.