3D Object Annotation via 2D Boundary Projection
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Solution Overview
Problem
Current methods for determining three-dimensional object boundaries in LiDAR point clouds are computationally expensive, require large memory storage, and necessitate human intervention, making full automation of the process challenging, especially in scenes with crowded objects.
Innovation Solution
A method and system that utilize both camera images and LiDAR point clouds to automatically annotate three-dimensional objects. This involves capturing images and point clouds, determining a two-dimensional boundary of objects in the images, identifying a subset of points within this boundary in the point clouds, and assigning unique identifiers to these points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional object boundary determination is performed in LiDAR point clouds using computational methods, then measurement precision is improved, but computing time increases and productivity decreases
Solution Approach 1:
The method segments the complex 3D point cloud processing into multiple stages: first projecting points to 2D image space, then performing 2D boundary detection, and finally mapping back to 3D space. This segmentation reduces computational complexity at each stage while maintaining boundary determination accuracy.
Solution Approach 2:
The patent introduces a 2D image projection as an intermediary representation between the 3D point cloud and the final 3D boundary determination. This intermediary 2D space serves as a computational bridge that simplifies the boundary detection process before mapping results back to 3D space.
2Measurement precision
If three-dimensional object boundary determination is performed in LiDAR point clouds, then measurement precision is improved, but memory storage requirements increase
Solution Approach 1:
The patent transforms the problem from 3D space to 2D space by projecting point cloud data onto an image plane. This dimensionality reduction decreases the data volume and memory requirements while preserving the essential geometric information needed for boundary determination.
3Measurement precision
If manual intervention is used for correcting and confirming three-dimensional object boundaries, then measurement precision is improved, but extent of automation decreases
Solution Approach 1:
The system performs self-correction and validation through the geometric consistency between the 2D projected boundaries and the original 3D point cloud data. The method automatically confirms boundaries by verifying that the projected 2D boundary accurately represents the 3D object structure, eliminating the need for manual intervention.
4Measurement precision
If three-dimensional object boundary determination is performed in crowded scenes with multiple objects, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the crowded scene into individual object regions by first detecting 2D boundaries in the projected image space, where objects are more easily separated. This segmentation in 2D space simplifies the subsequent 3D boundary determination for each object independently, reducing overall system complexity.
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
The approach reduces computational time and load, eliminates the need for human intervention, and improves the accuracy of three-dimensional object boundary determination and annotation, enabling more efficient and automated processing of complex scenes.
Implementation Method 1
capturing a point cloud representing the scene using a LiDAR system
Implementation Method 2
capturing an image of the scene using a camera
Data Source
AI summary
A method for automatically digitally annotating a three-dimensional object in a scene includes capturing an image of the scene using a camera, capturing a point cloud representing the scene using a LiDAR system, and determining a two-dimensional boundary of the three-dimensional object contained in the image. The method further includes determining a subset of points of the point cloud contained within the two-dimensional boundary and assigning a unique identifier to the subset of points contained within the two-dimensional boundary.