Cross-Sensor Annotation for 3D Search Space Reduction
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Solution Overview
Problem
Autonomous control systems face challenges in annotating sensor measurements, particularly from 3D sensors like LIDAR, which are difficult for human operators to label due to their complexity and the large amount of data involved, leading to suboptimal annotation accuracy and computational burdens.
Innovation Solution
An annotation system that uses annotations from 2D camera sensor measurements to identify a spatial region in 3D sensor measurements, narrowing down the search space for annotation models, thereby improving accuracy and reducing computational resources by applying the model only to the filtered region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If annotation models process the entire 3D sensor dataset, then comprehensive object detection is achieved, but computational resources and processing time increase significantly
Solution Approach 1:
The patent segments the 3D sensor data processing by dividing it into two stages: first processing 2D image data to identify candidate regions, then applying 3D annotation models only to those specific regions. This segmentation reduces the volume of data requiring computationally intensive 3D processing while maintaining comprehensive object detection coverage.
Solution Approach 2:
The patent performs preliminary annotation on 2D sensor data before processing 3D data. By first identifying objects of interest in the 2D domain and using those results to guide 3D processing, the system prepares the data in advance to minimize subsequent computational requirements for 3D annotation.
2Measurement precision
If human operators manually annotate 3D sensor measurements, then annotation accuracy can be maintained, but the time and effort required increase due to data complexity and volume
Solution Approach 1:
The patent segments the annotation task by having human operators annotate only 2D images rather than entire 3D point clouds. This division of labor allows human expertise to be applied where it is most effective (2D visual recognition) while avoiding the time-consuming task of manual 3D annotation.
Solution Approach 2:
The patent introduces 2D image annotations as an intermediary step between raw sensor data and final 3D annotations. These 2D annotations serve as guidance for generating or verifying 3D annotations, reducing the direct burden on human operators while maintaining accuracy.
3Ease of manufacture
If 3D sensor data is annotated directly without 2D reference, then independent annotation is achieved, but annotation difficulty increases due to missing sensor measurements and data format complexity
Solution Approach 1:
The patent merges 2D and 3D annotation processes by using 2D image annotations to guide and validate 3D annotations. This combination leverages the complementary strengths of both sensor types: the visual recognition capability of 2D cameras and the spatial depth information of 3D sensors.
Solution Approach 2:
The patent uses 2D image annotations as an intermediary reference that bridges the gap between easily annotated 2D data and difficult-to-annotate 3D data. This intermediary provides a reliable framework that ensures annotation completeness while simplifying the overall annotation process.
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.