Target Object Pose Estimation for Unseen Objects via Segmentation
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Solution Overview
Problem
Machine learning models for detecting the pose of a target object in an image suffer from overfitting to specific objects, leading to poor generalization to unseen objects, requiring additional information and retraining.
Innovation Solution
A method using a combination of coarse and dense segmentors to analyze images, where a coarse segmentor divides image patches by objects and a dense segmentor provides features, optimizing parameters for similarity and dissimilarity to determine the target object's pose without prior knowledge of its type or size, utilizing a canonical object model and region of interest for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained on specific objects to achieve satisfying pose detection performance, then the detection accuracy for those specific objects is improved, but the model suffers from poor generalization to unseen objects
Solution Approach 1:
The patent segments the pose detection task into two independent components: (1) object-agnostic segmentation to identify candidate regions in the input image, and (2) pose estimation on those segmented regions. By separating the segmentation step from the pose estimation step, the model achieves better generalization to unseen objects while maintaining high detection accuracy for known objects.
Solution Approach 2:
The patent introduces an intermediary segmentation step that produces candidate regions before pose estimation. This intermediary component acts as a bridge between the input image and the pose estimation model, enabling the system to adapt to new objects without retraining the entire model, thus improving generalization while preserving detection precision.
2Adaptability or versatility
If additional information about unseen objects is provided to improve generalization, then the model's ability to handle unseen objects is improved, but the complexity of the system increases
Solution Approach 1:
The patent enables the system to automatically adapt to unseen objects by using the segmentation output and pose estimation results to refine candidate regions iteratively. The system serves itself by learning from the input image without requiring external annotations or additional information about unseen objects, thereby improving generalization while avoiding increased system complexity.
Solution Approach 2:
Instead of requiring complete information about unseen objects, the patent uses partial information from the segmentation step and iteratively refines the pose estimation. This partial action approach allows the model to handle unseen objects effectively without needing comprehensive prior knowledge, thus improving adaptability without significantly increasing system complexity.
3Adaptability or versatility
If retraining of the model is performed to improve generalization to unseen objects, then the model's performance on unseen objects is improved, but the time and resources required are increased
Solution Approach 1:
The patent performs preliminary segmentation to identify candidate regions before pose estimation. This preliminary action creates a structured representation of the input image that can be reused across different objects, allowing the model to generalize to unseen objects without retraining. The segmentation step serves as a pre-computed foundation that reduces the need for time-consuming retraining.
Solution Approach 2:
The patent changes the operational parameters of the pose estimation model by using segmented candidate regions as input instead of raw images. This parameter change allows the model to adapt to different objects by changing the input format rather than retraining the model weights, significantly reducing the time and resources required for adaptation to unseen objects.
Data Source
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AI summary
A method (100) for determining the pose (2a) of a target object (2) in an input image (1), comprising the steps of: • providing (110) a set of reference images (3) that contain the target object (2) and are annotated with the respective pose (2a*) of the target object (2); • inputting (120) the reference images (3) to at least one segmentor (10, 11) that is configured to distinguish parts and/or features of an image belonging to different objects; • determining (130), from the output (10a, 11a) of the at least one segmentor (10, 11), a canonical object model (4) that pools information from the reference images (3) in one common coordinate system; • determining (140), from the output (10a, 11a) of the at least one segmentor (10, 11) in combination with the annotation (2a*) of the reference images (3), a representation (5) of the target object (2) in the output space of at least one segmentor (10, 11); • inputting (150) the input image (1) to the at least one segmentor (10, 11), and determining (160), based on a comparison of the resulting output (10b, 11b) of the at least one segmentor (10, 11) and the representation (5) of the target object (2), which parts (10c, 11c) of said resulting output (10b, 11b) of the at least one segmentor (10, 11) belong to the target object (2); • combining (170) said parts (10c, 11c) belonging to the target object (2) into a local reconstruction (6) of the target object (2); and • evaluating (180), from a comparison of the local reconstruction (6) and the canonical object model (4), the sought pose (2a) of the target object (2) in the input image (1).