3D Landmark Localization for Viewpoint-Robust Keypoint Detection
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Solution Overview
Problem
Existing machine learning models for landmark or keypoint localization face challenges such as loss of accuracy in images with defects, occlusions, deformable objects, and large viewpoint variations, and require extensive manual annotation, leading to inefficiencies and limited transferability.
Innovation Solution
A computer-implemented method that trains a machine learning model by uplifting point clusters to a three-dimensional space, applying transformations to enforce three-dimensional similarity, and using a loss function that penalizes differences between reconstructed and actual point clusters, combined with supervised and unsupervised training techniques to enhance keypoint detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unsupervised training methods are used for keypoint localization, then the model can handle diverse image variations, but the model lacks semantic consistency and human interpretability
Solution Approach 1:
The patent combines unsupervised training (which provides adaptability to image variations) with supervised training objectives (which provide semantic consistency). Specifically, it integrates reconstruction loss from unsupervised methods with supervision loss from annotated data, allowing the model to benefit from both approaches simultaneously.
Solution Approach 2:
The training framework is designed to be universal by accepting both annotated and unannotated images, allowing the same model to learn from diverse data sources. The multi-objective loss function can adaptively utilize different training signals depending on data availability.
2Measurement precision
If manually annotated images are included in the training dataset, then semantic consistency is improved, but the annotation process becomes highly burdensome and error-prone
Solution Approach 1:
The patent applies partial supervision by using only a subset of annotated images for training, rather than requiring comprehensive annotations for all training data. The model learns semantic consistency from the annotated portion while generalizing from the unannotated portion, reducing the annotation burden significantly.
Solution Approach 2:
The model performs self-supervised learning through reconstruction objectives on unannotated images, allowing it to learn from data without human intervention. This self-service capability reduces dependency on manual annotations while maintaining learning effectiveness.
3Measurement precision
If planar similarity transformation invariance is exploited for training, then keypoint detection is improved under small transformations, but the method fails under large viewpoint variations
Solution Approach 1:
The patent moves beyond planar (2D) similarity transformations to incorporate three-dimensional geometric transformations. By modeling keypoints in 3D space and using 3D transformation matrices, the system can handle large viewpoint variations that cannot be captured by 2D transformations alone.
Solution Approach 2:
The training objective is modified to use 3D transformation parameters instead of 2D parameters. This change in parameters allows the model to account for depth information and perspective effects, enabling accurate keypoint detection under large viewpoint changes.
4Measurement precision
If extensive manual annotation is performed to improve detection accuracy, then keypoint localization precision is improved, but the productivity and efficiency of the system decreases
Solution Approach 1:
The system performs self-supervised learning using reconstruction objectives on unannotated images, eliminating the need for extensive manual annotation. The model learns meaningful representations automatically from the data itself, maintaining high accuracy while significantly improving productivity.
Solution Approach 2:
The training framework is designed to efficiently utilize both annotated and unannotated data, creating a universal training pipeline that maximizes learning from available data without requiring proportional annotation effort. This multi-functional approach maintains accuracy while improving system efficiency.
Data Source
AI summary
A computer-implemented method comprises obtaining a source image and a target image, determining a first point cluster by processing the source image using a machine learning mode, determining a second point cluster by processing the target image using the machine learning model, uplifting the first point cluster to obtain an uplifted first point cluster, uplifting the second point cluster to obtain an uplifted second point cluster, estimating a transformation that maps the uplifted first point cluster to the uplifted second point cluster, applying the estimated transformation to the uplifted first point cluster to obtain a candidate reconstruction of the uplifted second point cluster, and updating the machine learning model to reduce a loss function including a term penalizing a difference between the uplifted second point cluster and the candidate reconstruction of the uplifted second point cluster.