3D Dynamic Object Labeling with Key Frames and Point Cloud Interpolation
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Solution Overview
Problem
Existing methods for labeling dynamic objects in point cloud data are labor-intensive, require significant computing power, and often produce inaccurate prediction boxes, especially when dealing with irregular and high-dimensional data, leading to inefficiencies and poor model adaptation across different data domains.
Innovation Solution
A method that transforms local point clouds into a global coordinate system, selects key frames for manual labeling, interpolates other frames, and uses point cloud registration to generate accurate 3D prediction boxes through manual and automatic interpolation, reducing the need for manual intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep learning algorithms are used for automatic labeling of point cloud dynamic objects, then labeling speed is improved, but labeling accuracy deteriorates and requires significant computing power and manual refinement
Solution Approach 1:
The patent introduces an intermediary manual labeling step for key frames between automatic deep learning labeling and final output. The system automatically labels most frames using deep learning, but inserts manual labeling at key frames as an intermediary step to correct errors and improve accuracy, then propagates these corrected labels through interpolation to other frames.
Solution Approach 2:
The patent segments the labeling process into different types of frames: key frames that require manual labeling for high accuracy, and non-key frames that use automatic deep learning labeling. This segmentation allows the system to apply different labeling strategies to different parts of the data, improving overall accuracy while maintaining efficiency.
2Measurement precision
If manual labeling is performed for all frames to ensure high label quality, then labeling accuracy is improved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent applies partial manual action by performing manual labeling only on key frames rather than all frames. This partial approach provides sufficient label quality for training while dramatically reducing the time consumption and labor intensity compared to complete manual labeling of every frame.
Solution Approach 2:
The patent performs preliminary manual labeling on key frames first, then uses interpolation techniques to propagate these labels to non-key frames. This preliminary action on critical frames provides a foundation that reduces the need for extensive manual labeling across the entire dataset.
3Adaptability or versatility
If deep learning models are refined for new data batches to adapt to different data fields, then model accuracy is improved, but computing power requirements and training time increase
Solution Approach 1:
The patent performs preliminary manual labeling on key frames from new data batches, then uses interpolation to generate labels for the entire batch. This preliminary action provides sufficient training data for model adaptation without requiring extensive retraining on all data, reducing computing power consumption while maintaining adaptability.
Data Source
AI summary
Disclosed is interactive labeling of a 4D dynamic object based on time series data, which aims at time series-related point cloud dynamic object data. Multi-frame local point clouds in the same time series are transformed into the same global coordinate system with corresponding poses to obtain global point clouds in the same time series are obtained, which clearly shows the moving trajectory of the dynamic object. Taggers can label key 3D boxes based on the moving trajectory of the dynamic object, and automatically generate 3D prediction boxes of other frames based on these key 3D boxes, which significantly reduces the number of frames that need manual operation and solves the problem that 3D prediction boxes generated based on deep learning model are inaccurate and efficiency can hardly be improved.