3D Depth Map Annotation for Static Object Labeling
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Solution Overview
Problem
Existing methods for annotating images for machine learning models, particularly for real-time object recognition in autonomous vehicles, are labor-intensive and struggle to accurately identify and annotate objects, especially under harsh conditions or when objects are small or partially obscured, leading to inadequate training datasets.
Innovation Solution
The method involves generating a three-dimensional stacked scene representation from digital images and dense depth maps, associating each point with a stability score, removing unstable points, and using this representation to detect and annotate static objects, thereby creating a larger, more accurate training dataset for machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual annotation methods are used for training machine learning models, then annotation accuracy can be maintained, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system performs self-annotation by automatically generating three-dimensional stacked scene representations from captured images and depth maps, computing stability scores, and identifying static objects without human intervention. This self-service mechanism resolves the contradiction by making the annotation process autonomous while maintaining high productivity and acceptable accuracy through algorithmic object detection and classification
Solution Approach 2:
The patent transitions from two-dimensional image annotation to three-dimensional stacked scene representation. By stacking multiple image frames into a 3D volume and computing stability scores across temporal dimensions, the system enables automatic differentiation between static and dynamic objects, thereby achieving automated annotation at scale without manual labor
2Productivity
If automated annotation methods are used to increase productivity, then annotation speed improves, but annotation accuracy deteriorates
Solution Approach 1:
The three-dimensional stacked scene representation serves as an intermediary structure between raw image data and final object annotations. By introducing depth maps and stability score computations as intermediate processing steps, the system enhances automated detection accuracy while maintaining high throughput, resolving the contradiction between automation and precision
Solution Approach 2:
The system performs preliminary actions by pre-computing stability scores for all points in the three-dimensional stacked scene before final object detection. This preliminary classification of static versus dynamic points enables more accurate automated annotation by filtering out moving objects that would otherwise interfere with static object detection, thereby improving measurement precision without sacrificing productivity
3Device complexity
If traditional two-dimensional annotation is used, then the process is simpler, but the ability to distinguish static objects from dynamic objects deteriorates
Solution Approach 1:
The patent adds temporal and depth dimensions to traditional two-dimensional annotation by creating three-dimensional stacked scenes from multiple image frames. This dimensional enhancement enables reliable distinction between static and dynamic objects through stability score computation, resolving the contradiction by showing that increased dimensionality improves reliability while managing complexity through algorithmic processing
Solution Approach 2:
The system introduces dynamic stability score computation that adapts to each scene by analyzing temporal consistency across image frames. This dynamic approach automatically adjusts to differentiate static objects (high stability) from dynamic objects (low stability), improving identification reliability while keeping the system complexity manageable through adaptive rather than rigid processing rules
Data Source
AI summary
There is provided a method for annotating digital images for training a machine learning model, comprising: generating, from digital images and a plurality of dense depth maps, each associated with one of the digital images, an aligned three-dimensional stacked scene representation of a scene, where the digital images are captured by sensor(s) at the scene, and where each point in the three-dimensional stacked scene is associated with a stability score indicative of a likelihood the point is associated with a static object of the scene, removing from the three-dimensional stacked scene unstable points to produce a static three-dimensional stacked scene, detecting in at least one of the digital images static object(s) according to the static three-dimensional stacked scene, and classifying and annotating the static object(s). The machine learning model may be trained on the images annotated with a ground truth of the static object(s).