3D Surface Modeling From Sparse Laser Scan and Image Segments
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Solution Overview
Problem
Existing methods for three-dimensional object identification using sparse laser scan data are inadequate, as they fail to accurately capture information about objects due to sparse data collection and lack of depth information in two-dimensional image data, leading to unreliable object identification.
Innovation Solution
A method involving segmenting two-dimensional images into image segments, generating image segment frustums, and deriving three-dimensional surfaces from sparse laser scan data to enhance object identification by pairing pixels with three-dimensional coordinate points, incorporating texture and depth information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If sparse laser scan data is used for three-dimensional object identification, then data collection time is reduced, but measurement precision and reliability of object identification deteriorate
Solution Approach 1:
The patent segments the two-dimensional image into multiple image segments, each corresponding to a specific region of interest. This segmentation allows the system to process and match features more efficiently, improving object identification accuracy from sparse laser scan data by focusing computational resources on relevant regions rather than processing the entire image uniformly.
Solution Approach 2:
The patent introduces two-dimensional image data as an intermediary to bridge the gap between sparse laser scan data and complete object identification. The 2D images provide additional texture and visual information that compensates for the sparsity of 3D laser points, enabling reliable object identification even when laser scan data is limited.
2Loss of information
If two-dimensional image data is used to supplement laser scan data, then texture information is improved, but depth information remains insufficient
Solution Approach 1:
The patent merges 2D image data with 3D laser scan data into a unified representation. The 2D images are projected onto the 3D point cloud, combining texture information from images with depth information from laser scans. This fusion creates a more complete object model that leverages the strengths of both data sources.
Solution Approach 2:
The patent transitions from two-dimensional image data to three-dimensional representation by projecting 2D image features onto 3D laser scan points. This dimensional transformation allows texture information from 2D images to be associated with corresponding 3D surface locations, enriching the spatial understanding while maintaining depth accuracy.
3Measurement precision
If image segmentation is performed to match pixels with three-dimensional coordinate points, then pairing accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent divides the image into multiple segments and processes each segment separately to match with corresponding 3D coordinate points. This segmentation reduces the computational complexity of pixel-coordinate pairing by breaking down the large-scale matching problem into smaller, more manageable sub-problems, while maintaining or improving pairing accuracy through focused processing.
Solution Approach 2:
The patent performs image segmentation and pixel-coordinate matching only for regions that contain relevant object information, rather than processing the entire image uniformly. This partial action approach reduces unnecessary computational overhead while maintaining pairing accuracy for the critical regions where object identification is needed.
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
Improves the accuracy of three-dimensional object identification by increasing the number of three-dimensional coordinate points and providing reliable pairing between two-dimensional image pixels and three-dimensional objects, even with sparse laser scan data.
Implementation Method 1
The time taken for the reflected laser beam to be received by the laser scanner may be used to measure a distance between the laser scanner and the point from which the laser beam was reflected
Implementation Method 2
cameras that are configured to collect two-dimensional data about the same environment in which the laser scanner is operating
Data Source
AI summary
A method may include obtaining three-dimensional coordinate points and a two-dimensional image representing an environment and objects in the environment. The method may include segmenting the image into image segments and obtaining a pixel selection in the image. The method may include generating an image segment frustum corresponding to a particular image segment shape that includes the pixel. The method may include intersecting the image segment frustum with the coordinate points to determine a subset of the three-dimensional coordinates used to fit a derived surface. The derived surface may represent a three-dimensional volume having a surface shape corresponding to the shape of the image segment frustum and may include a subset of three-dimensional coordinate points that intersects with the image segment frustum. The method may include generating a derived three-dimensional coordinate point that represents a surface point included in the environment within a volume of the derived surface.