6D Pose Estimation via 3D-OCR and GeoReS Modules
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Solution Overview
Problem
Current methods for category-level 6D object pose and size estimation face challenges such as limited generalization to unseen instances, intra-category variations, and the synthetic-real image domain gap, particularly in utilizing depth information and handling symmetry in objects.
Innovation Solution
A geometry-based approach that includes a 3D Orientation-Consistent Representation (3D-OCR) module for learning implicit orientation alignment, a Geometry-constrained Reflection Symmetry (GeoReS) module for global shape understanding, and a Mirror-Paired Dimensional Estimation (MPDE) module for size regression, using depth images without external pose-annotated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If category-level pose estimation methods are used to generalize to unseen instances, then adaptability improves, but measurement precision deteriorates
Solution Approach 1:
The method segments the pose estimation problem into two independent stages: first estimating 6D pose parameters, then separately estimating size parameters. This segmentation allows each stage to specialize in its specific task, improving overall precision while maintaining generalization capability through the modular architecture.
Solution Approach 2:
The method transitions from directly regressing 3D size parameters to first estimating 2D bounding box dimensions from the detected image, then computing the third dimension based on the estimated 6D pose and known 3D model. This dimensional transformation simplifies the regression problem and improves accuracy by leveraging the geometric constraints of the object.
2Measurement precision
If depth information is utilized for pose estimation, then measurement precision improves, but device complexity worsens
Solution Approach 1:
The method introduces an intermediate 2D bounding box detection step that serves as a mediator between the input image and the final 3D pose estimation. This intermediary representation simplifies the integration of depth information by providing a constrained search space, thereby improving precision without proportionally increasing system complexity.
3Measurement precision
If 3D model information is used for instance-level pose estimation, then measurement precision improves, but adaptability worsens
Solution Approach 1:
The method creates a universal framework that can handle both instance-level and category-level pose estimation. By estimating 6D pose first and then deriving size information, the system can work with any 3D model within a category, achieving multi-functionality that improves adaptability while maintaining precision through the consistent estimation pipeline.
4Device complexity
If direct 3D size regression is performed, then device complexity is reduced, but measurement precision worsens
Solution Approach 1:
The method changes the dimensionality of the regression problem by first estimating 2D bounding box dimensions from the image, then computing the 3D size parameters using the estimated pose and 3D model information. This dimensional transformation reduces the complexity of direct 3D regression while significantly improving size estimation accuracy through geometric constraints.
Data Source
AI summary
A network for category-level 6D pose and size estimation, including a 3D-OCR module for 3D Orientation-Consistent Representation, a GeoReS module for Geometry-constrained Reflection Symmetry, and a MPDE module for Mirror-Paired Dimensional Estimation; wherein the 3D-OCR module and the GeoReS module are incorporated in parallel; the 3D-OCR module receives a canonical template shape including canonical category-specific keypoints; the GeoReS module receives an original input depth observation including pre-processed predicted category labels and potential masks of the target instances; the MPDE module receives the output from the GeoReS module as well as the original input depth observation; and the network outputs the estimation results based on the output of the MPDE module, the output of the 3D-OCR module, as well as the canonical template shape. Also provided are corresponding systems and methods.