3D Articulation Model Generation for Multi-Part Objects
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Solution Overview
Problem
Conventional machine learning models are limited in generating 3D representations of articulated objects, as they can only handle specific types of objects and single articulations, lacking generalizability and the ability to handle multiple movable parts.
Innovation Solution
A computer-implemented method for generating an articulation model by receiving images of an object in different articulations, generating 3D geometry from these images, and creating an articulation model that includes a segmentation model and motion parameters for each part, allowing for accurate representation of objects with multiple articulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional machine learning model is trained to generate 3D representations of a particular type of object, then the model can generate accurate 3D representations for that specific object type, but the model cannot generalize to other types of objects or handle multiple articulations
Solution Approach 1:
The patent segments the articulated object into multiple rigid parts connected by joints, with each part having its own 3D representation and motion parameters. This segmentation allows the system to handle complex articulated objects with multiple movable parts independently, enabling generalization across different object types while maintaining accurate 3D representations for each segment.
Solution Approach 2:
The patent introduces dynamic motion parameters that describe the rotational and translational movements of each articulated part relative to others. This dynamic approach allows the system to capture the temporal evolution of articulation states, enabling the model to handle multiple articulations and generalize to different object types while maintaining precision in 3D representation.
2Measurement precision
If a conventional machine learning model is trained for single articulation objects, then the model can generate 3D representations accurately for that case, but the model fails when encountering objects with multiple movable parts
Solution Approach 1:
The patent divides the articulated object into multiple rigid parts, each with its own 3D representation and motion parameters. This segmentation enables the system to handle objects with multiple movable parts by processing each part independently while maintaining accurate 3D reconstructions through consistent segmentation across different articulation states.
Solution Approach 2:
The patent performs preliminary segmentation and motion parameter estimation for each articulation state before generating the final 3D representation. This preliminary action allows the system to prepare and align the 3D geometries of different parts across multiple articulation states, enabling accurate reconstruction of objects with multiple movable parts while maintaining reconstruction precision.
Data Source
AI summary
One embodiment of a method for generating an articulation model includes receiving a first set of images of an object in a first articulation and a second set of images of the object in a second articulation, performing one or more operations to generate first three-dimensional (3D) geometry based on the first set of images, performing one or more operations to generate second 3D geometry based on the second set of images, and performing one or more operations to generate an articulation model of the object based on the first 3D geometry and the second 3D geometry.