3D Structure Annotation for Fast Multi-Frame Perception Labeling
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Solution Overview
Problem
The manual process of creating annotated training examples for machine learning perception components, particularly in multiple perception modalities, is time-consuming and costly, limiting the number of training images and affecting the performance of the perception component.
Innovation Solution
A computer-implemented annotation tool that facilitates fast and efficient annotation of perception inputs by generating 3D models from 3D structure points, allowing for automatic or semi-automatic alignment and annotation of common structure components across multiple frames, enabling efficient creation of 2D and 3D annotation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation process is used for creating annotated training examples, then annotation accuracy can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The system creates a 3D model copy of the structure component from a reference frame and uses this model to generate annotations in target frames. This copying approach maintains annotation accuracy by preserving structural characteristics while eliminating the need for manual annotation in each frame, significantly reducing time consumption.
Solution Approach 2:
The system performs preliminary action by creating the 3D model from a reference frame in advance. This pre-created model is then reused across multiple target frames, avoiding repetitive manual annotation work and reducing overall time consumption while maintaining consistency and accuracy.
2Adaptability or versatility
If manual annotation is performed for multiple perception modalities, then comprehensive training data can be obtained, but cost and time requirements become prohibitive
Solution Approach 1:
The 3D model serves multiple functions across different perception modalities. The same model is used for both 2D image annotation (through projection) and 3D point cloud annotation (through direct alignment), providing universal applicability that improves productivity while maintaining comprehensive multi-modality coverage.
Solution Approach 2:
The system merges the annotation process for multiple perception modalities into a unified workflow. By combining 2D and 3D annotation tasks into a single automated process using the 3D model, the system achieves both multi-modality coverage and high productivity simultaneously.
3Productivity
If 3D models are generated and aligned automatically, then annotation speed increases, but alignment precision may be affected
Solution Approach 1:
The system uses feedback from the alignment process itself to improve precision. By automatically aligning the 3D model with structure components in target frames and using the alignment results to refine the model position, the system achieves both high annotation speed and precise alignment without requiring manual intervention.
Data Source
AI summary
A computer-implemented method of creating one or more annotated perception inputs, the method comprising, in an annotation computer system: receiving a plurality of captured frames, each frame comprising a set of 3D structure points, in which at least a portion of a common structure component is captured; computing a reference position within at least one reference frame of the plurality of frames; generating a 3D model for the common structure component by selectively extracting 3D structure points of the reference frame based on the reference position within that frame; determining an aligned model position for the 3D model within a target frame of the plurality of frames based on an automatic alignment of the 3D model with the common structure component in the target frame; and storing annotation data of the aligned model position in computer storage, in association with at least one perception input of the target frame for annotating the common structure component therein.