Dynamic Regression Forest for 3D Prediction in Motion
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Solution Overview
Problem
Existing methods for predicting three-dimensional representations in three-dimensional scenes, such as those used in robot monitoring and augmented reality, struggle with dynamic scenes where objects are in motion, leading to reduced accuracy and robustness.
Innovation Solution
A process and device for predicting three-dimensional representations using a forest of regression trees, which dynamically updates based on the evaluation of possible associations between two-dimensional and three-dimensional representations, incorporating parameters to assess the status, consecutive prediction errors, and extracted two-dimensional representations to improve discriminative power and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a static regression tree forest is used for predicting 3D representations, then the model structure remains simple and computationally efficient, but the accuracy deteriorates when objects are in motion
Solution Approach 1:
The patent transforms the static regression tree forest into a dynamic structure that automatically adapts to motion detected in the scene. The system monitors object motion and selectively updates only the relevant portions of the regression model, allowing the model to adapt to dynamic scenes without requiring complete retraining, thus maintaining accuracy while controlling complexity
Solution Approach 2:
The patent changes parameters of the regression tree forest dynamically based on motion detection. When motion is detected, the system modifies specific parameters of the regression model related to moving objects, such as updating transformation matrices or adjusting feature weights, rather than restructuring the entire model, thereby improving accuracy with minimal complexity increase
2Reliability
If the regression model is continuously updated to adapt to motion, then the robustness improves in dynamic scenes, but the computational time and processing complexity increase
Solution Approach 1:
The patent applies partial updates to the regression model rather than complete retraining. The system identifies only the specific portions of the model that need updating based on motion detection, such as updating only the transformation parameters for moving objects while keeping the rest of the model intact, thus achieving robustness with minimal computational overhead
Solution Approach 2:
The patent implements continuous adaptation through incremental updates. The system continuously monitors scene changes and performs small incremental updates to the regression model in real-time, maintaining robustness to motion while avoiding the computational burden of periodic complete retraining, thus achieving continuous improvement with low processing time cost
3Measurement precision
If deep learning methods are used for 3D localization, then the accuracy improves for structured features, but the method fails to localize moving 3D structures
Solution Approach 1:
The patent extends the regression tree forest to dynamically adapt to motion by incorporating motion detection and selective model updates. This allows the system to maintain high localization accuracy for both static and moving 3D structures, resolving the limitation of static deep learning models that cannot handle dynamic scenes
Solution Approach 2:
The patent implements a feedback mechanism where motion detection results are fed back into the regression model update process. The system uses motion information to identify which parts of the scene have changed and selectively updates the model parameters accordingly, enabling accurate localization of moving objects while maintaining the precision benefits of deep learning approaches
Data Source
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AI summary
The invention relates to a method for predicting a three-dimensional (3D) depiction in a three-dimensional scene, using one or more two-dimensional (2D) depictions obtained from at least one image generated by a camera. The method comprises obtaining a forest of regression trees and, for said at least one image: extracting a 2D depiction associated with one or more positions within the image, predicting (236) a 3D depiction, corresponding to the extracted 2D depiction, using the forest of regression trees, which defines a set of possible associations between at least one 2D depiction and a 3D depiction, each possible association resulting from a predictive model, evaluating one or more of the possible associations defined by the forest of regression trees using a predetermined confidence criterion, and updating the forest of regression trees, this comprising deactivating one of more possible associations depending on the evaluation of the possible associations.