Cross-Labeling 2D Camera and 3D LiDAR Annotations
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Solution Overview
Problem
Existing autonomous control systems face challenges in accurately annotating sensor measurements from active sensors like LIDAR due to their 3D nature, which is difficult for human operators and computationally burdensome for annotation models, leading to suboptimal accuracy and resource inefficiency.
Innovation Solution
An annotation system uses annotations from a first set of 2D sensor measurements, such as camera images, to determine a spatial region in 3D measurements from active sensors like LIDAR, narrowing the search area for annotations and applying an annotation model only to this filtered region, thereby improving accuracy and reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If annotation models are applied to entire 3D LIDAR sensor measurements, then comprehensive coverage is achieved, but computational load and processing time increase significantly
Solution Approach 1:
The patent segments the 3D LIDAR sensor measurements into multiple regions based on 2D camera image annotations. Instead of processing the entire 3D point cloud, the system divides it into region-of-interest segments corresponding to detected 2D objects, thereby reducing computational load while maintaining annotation accuracy for relevant areas.
Solution Approach 2:
The patent extracts and processes only the relevant portions of 3D LIDAR data that correspond to objects detected in 2D camera images. By taking out and focusing computational resources on specific regions containing objects of interest, the system achieves accurate annotations without the computational burden of processing all 3D measurements.
2Measurement precision
If annotation models are applied to entire 3D LIDAR sensor measurements, then comprehensive coverage is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent segments the 3D LIDAR sensor measurements into multiple regions based on 2D camera image annotations. Instead of processing the entire 3D point cloud, the system divides it into region-of-interest segments corresponding to detected 2D objects, thereby reducing computational load while maintaining annotation accuracy for relevant areas.
Solution Approach 2:
The patent extracts and processes only the relevant portions of 3D LIDAR data that correspond to objects detected in 2D camera images. By taking out and focusing computational resources on specific regions containing objects of interest, the system achieves accurate annotations without the computational burden of processing all 3D measurements.
3Measurement precision
If human operators manually annotate 3D LIDAR sensor measurements, then annotation quality can be ensured, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The patent performs preliminary annotation using 2D camera images before annotating 3D LIDAR data. By first detecting objects in 2D images and using those annotations to guide 3D annotation, the system reduces the manual effort required for 3D annotation while maintaining quality, as human operators only need to verify and refine pre-identified regions.
Solution Approach 2:
The patent uses 2D camera image annotations as an intermediary to facilitate 3D LIDAR annotation. The 2D annotations serve as a bridge that guides the annotation process for 3D data, allowing human operators to leverage their 2D annotation skills while efficiently producing 3D annotations through the intermediary 2D results.
Data Source
AI summary
An annotation system uses annotations for a first set of sensor measurements from a first sensor to identify annotations for a second set of sensor measurements from a second sensor. The annotation system identifies reference annotations in the first set of sensor measurements that indicates a location of a characteristic object in the two-dimensional space. The annotation system determines a spatial region in the three-dimensional space of the second set of sensor measurements that corresponds to a portion of the scene represented in the annotation of the first set of sensor measurements. The annotation system determines annotations within the spatial region of the second set of sensor measurements that indicates a location of the characteristic object in the three-dimensional space.