3D Object Pose Estimation via Dynamic Feature Area Selection
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Solution Overview
Problem
Existing methods for estimating the position and orientation of three-dimensional objects using visual information face challenges such as biased feature point selection, limited viewpoint visibility, and increased angular resolution difficulties due to higher shooting angles, leading to inaccurate orientation discrimination.
Innovation Solution
An information processing apparatus that selects specific areas for feature extraction, learns a detection model for these areas, generates area combinations, recognizes the object based on these combinations, and dynamically adds new areas to improve estimation accuracy, thereby reducing viewpoint bias and enhancing angular resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all orientations are discriminated as different classes with increased angular resolution, then the accuracy of orientation estimation is improved, but the number of similar images having different orientations increases making it difficult to identify the orientation
Solution Approach 1:
The patent segments the orientation space by selecting only critical feature points that are most useful for distinguishing orientations, rather than treating all orientations as equally distinct classes. This segmentation approach reduces the effective number of distinguishable orientations while maintaining measurement precision through focused analysis of key features.
Solution Approach 2:
The patent extracts and selects only the most useful feature points from the entire set of possible features. By taking out only the critical feature points that provide maximum orientation discrimination capability, the system reduces the complexity of comparing similar images while maintaining high angular resolution for orientation estimation.
2Ease of operation
If feature points are selected manually and intentionally, then the selection process is simple and controllable, but the selected feature points may be biased depending on the viewpoint and limited in visibility
Solution Approach 1:
The patent implements self-service by enabling the system to automatically select its own feature points based on their usefulness for orientation estimation. The feature point selection is performed automatically by analyzing which points provide the most discriminative information, eliminating manual intervention while ensuring viewpoint-independent reliability through objective selection criteria.
Solution Approach 2:
The patent changes the selection criterion from manual intuition to an automatic parameter-based approach that evaluates feature points based on their usefulness for orientation estimation. This parameter change enables the system to adaptively select feature points that remain reliable across different viewpoints, overcoming the limitations of manual selection.
3Measurement precision
If many feature points are selected to improve accuracy, then the estimation precision is improved, but the feature points may be visible from a limited number of viewpoints
Solution Approach 1:
The patent applies dynamics by making the feature point selection adaptive rather than static. The system dynamically selects feature points based on their visibility and usefulness for the current estimation task, allowing the same system to effectively use many feature points when visible while automatically adapting when viewpoint coverage changes. This dynamic approach maintains both high accuracy and broad viewpoint coverage.
Data Source
AI summary
An information processing apparatus includes a selection unit configured to select a plurality of specific areas of a target object, a learning unit configured to learn a detection model that relates to each of the plurality of specific areas, a generation unit configured to generate an area combination as a combination of specific areas selected from the plurality of specific areas, a recognition unit configured to recognize the target object based on the detection model and the area combination, and an addition unit configured to add a new specific area based on a recognition result obtained by the recognition unit. If the new specific area is added by the addition unit, the learning unit further learns a detection model that relates to the new specific area.