3D Annotation Generation via Wire Model Alignment
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Solution Overview
Problem
Current machine learning models for computer vision require large amounts of high-quality annotation data, which is typically manually generated, leading to inefficiencies in resource usage and potential errors, slowing the deployment of models and resulting in poor-quality data that affects recognition accuracy.
Innovation Solution
An annotation system that generates three-dimensional annotations by aligning and interpolating wire models in video data using camera information, transforming 2D data into 3D representations to create high-quality annotation data efficiently and accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to generate training data, then annotation quality can be controlled, but resource consumption increases and productivity decreases
Solution Approach 1:
The system enables automatic self-annotation of 3D objects by processing video data and camera information without requiring manual human intervention. The automated system processes video frames, extracts object information, generates 3D wireframe models, and creates annotations independently, thereby eliminating the bottleneck of manual annotation while maintaining high quality through algorithmic precision.
Solution Approach 2:
The patent replaces the mechanical manual annotation process with an automated computational system. Instead of human annotators manually marking objects, the system uses computer vision algorithms, 3D reconstruction techniques, and wireframe rendering to automatically generate annotations, substituting human labor with automated mechanical processes.
2Manufacturing precision
If manual annotation is used, then annotation accuracy can be maintained, but time consumption increases
Solution Approach 1:
The system continuously processes video data in real-time, extracting object information and generating annotations without interruption. The automated pipeline operates continuously on video streams, processing frames sequentially and generating 3D annotations without the time losses associated with manual review and verification processes.
Solution Approach 2:
The system performs preliminary processing of video data, including object detection, tracking, and 3D reconstruction, before generating final annotations. This preliminary action prepares the data in advance, allowing for rapid annotation generation without requiring time-consuming manual intervention during the annotation process itself.
3Productivity
If automated annotation systems are implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The automated annotation system is divided into distinct functional modules: video processing module, object detection module, 3D reconstruction module, wireframe generation module, and annotation output module. Each module handles a specific task independently, making the complex system manageable through modular architecture while maintaining high productivity through automated processing.
4Reliability
If high-quality annotation data is generated manually, then model training accuracy improves, but resource consumption increases
Solution Approach 1:
The automated system generates high-quality annotations through self-processing of video data, eliminating the need for human annotators. The system uses computational algorithms to achieve consistent, high-quality annotations that match or exceed manual quality, while significantly reducing the resource consumption associated with human labor and verification processes.
Data Source
AI summary
A device may receive a video and corresponding camera information associated with a camera that captured the video, and may select an object in the video and a wire model for the object. The device may adjust an orientation, location, or size of the wire model to align the wire model on the object in a frame of the video, based on the corresponding camera information and to generate an adjusted wire model. The device may identify the object in another frame of the video, and may align the adjusted wire model on the object in the other frame. The device may interpolate the adjusted wire model for the object for intermediate frames of the video between the first and other frames, and may generate three-dimensional annotations for the video based on the adjusted wire models. The device may train a machine learning model based on the three-dimensional annotations.