Multi-Stage Object Pose Estimation via 3D Voxel Template Matching
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Solution Overview
Problem
Current object detection and multi-dimensional pose estimation methods struggle with scalability and generalization to new objects, often requiring lengthy annotation of real data or rendering of synthetic training data, and are typically object-specific.
Innovation Solution
The proposed method generates a plurality of templates from a 3D model of an object rendered from different virtual viewpoints, allowing for template matching and correspondence determination between image pixels and 3D model voxels, enabling multi-dimensional pose estimation without the need for specific training on the target object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard 2D detectors and generalized template matching are used for new objects, then object localization is achieved, but rotation estimation accuracy deteriorates
Solution Approach 1:
The patent transitions from 2D image space to 3D voxel space by rendering the detected object as a 3D voxel model and comparing it with template voxel models from multiple viewpoints. This dimensional elevation enables accurate 6D pose estimation including rotation, overcoming the limitations of 2D-based methods.
Solution Approach 2:
The patent pre-renders multiple template views of the object from different known viewpoints and stores them as voxel models before actual pose estimation. This preliminary preparation of templates enables rapid and accurate rotation estimation without requiring real-time rendering or complex optimization during detection.
2Measurement precision
If high accuracy 6D pose estimation is pursued, then measurement precision improves, but scalability and generalization to new objects deteriorate
Solution Approach 1:
The patent creates a 3D voxel copy of the detected object in the image and compares it with pre-rendered template voxel copies. This copying approach enables the method to generalize to new objects without requiring retraining, as the voxel-based representation and template matching process are object-agnostic and rely only on the availability of a 3D model.
Solution Approach 2:
The patent develops a universal pose estimation framework that works across different object categories by using 3D voxel representations and template matching. The method is not tied to specific object types or requiring object-specific training, making it broadly applicable to any object for which a 3D model is available.
3Measurement precision
If object-specific training is performed to improve accuracy, then measurement precision improves, but productivity and scalability deteriorate
Solution Approach 1:
The patent pre-renders multiple template views of the object from different known viewpoints and stores them as voxel models before actual pose estimation. This preliminary preparation of templates enables rapid and accurate rotation estimation without requiring real-time rendering or complex optimization during detection.
Solution Approach 2:
The patent replaces iterative mechanical optimization processes with a direct voxel-based template matching approach. By comparing 3D voxel representations directly, the method eliminates the need for time-consuming gradient-based optimization while maintaining high accuracy in 6D pose estimation.
4Adaptability or versatility
If extensive data annotation and synthetic rendering are used to improve generalization, then adaptability improves, but loss of time and productivity deteriorate
Solution Approach 1:
The patent creates a 3D voxel copy of the detected object in the image and compares it with pre-rendered template voxel copies. This copying approach enables the method to generalize to new objects without requiring retraining, as the voxel-based representation and template matching process are object-agnostic and rely only on the availability of a 3D model.
Solution Approach 2:
The patent enables the system to generate its own training templates by rendering 3D models from multiple viewpoints, eliminating the need for manual data annotation or external synthetic data preparation. The method is self-sufficient in creating the necessary template library for pose estimation.
Data Source
AI summary
Various embodiments include methods for estimating a multi-dimensional pose of an object based on an image of the object. An example includes: providing the image depicting the object and a plurality of templates TEMPL(i), including generating the templates TEMPL(i) from a 3D model of the object in a rendering procedure, wherein different templates TEMPL(i), TEMPL(j) with i≠j of the plurality are generated by rendering from different known virtual viewpoints vVIEW(i), vVIEW(j) on the model; matching templates wherein at least one template TEMPL(J) from the plurality of templates TEMPL(i) is identified which matches best with the image; determining correspondence by comparing a representation of the identified template TEMPL(J) with a representation of the image to determine 2D-3D-correspondences between pixels in the image and voxels of the 3D model of the object; and estimating a multi-dimensional pose based on the 2D-3D-correspondences.