Pose Estimation Using 3D Gaussian Splatting for Novel Objects
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Solution Overview
Problem
Existing object pose estimation techniques using machine learning models are limited to predicting poses for the same object or category of objects used in training, and fail to accurately estimate poses for novel objects or objects of different types.
Innovation Solution
A computer-implemented method for determining object poses that involves receiving an image of an object, sampling an initial pose, and updating it using a render and compare technique with transformer encoders to determine a refined pose, allowing for the prediction of poses for different objects and objects of varying types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained using images of a given object and pose labels, then the model can correctly predict poses for the same object or category, but the model fails to accurately estimate poses for novel objects or objects of different types
Solution Approach 1:
The patent introduces a 3D Gaussian Splatting model as an intermediary representation that bridges the gap between trained object categories and novel objects. Instead of directly predicting poses for unseen objects, the system first generates a 3D Gaussian Splatting representation from training data, then uses this representation to predict poses for both known and novel objects, enabling zero-shot generalization.
Solution Approach 2:
The patent changes the representation parameters from traditional 2D image-based pose estimation to 3D Gaussian Splatting parameters. This parameter transformation allows the model to capture three-dimensional geometry and appearance properties that are invariant across different object categories, enabling accurate pose prediction for novel objects without retraining.
2Productivity
If traditional pose estimation methods are used, then the system can process images efficiently, but it cannot handle objects without CAD models or with complex geometries
Solution Approach 1:
The patent replaces traditional mechanical/CAD-based pose estimation systems with a neural 3D Gaussian Splatting representation system. This substitution eliminates the dependency on pre-existing CAD models and allows the system to handle any object geometry by learning from training images, while maintaining efficient processing through the compact Gaussian Splatting representation.
3Adaptability or versatility
If a comprehensive training dataset covering all object categories is used, then the model can accurately predict poses for diverse objects, but the training time and computational resources increase significantly
Solution Approach 1:
The patent creates a universal 3D Gaussian Splatting representation that serves multiple functions: it acts as both the object model and the pose prediction basis. This universal representation learned from a diverse training set can predict poses for any object category without requiring category-specific models, enabling single-shot training on diverse objects while maintaining broad applicability.
Data Source
AI summary
One embodiment of a method for determining object poses includes receiving a first image of an object, sampling an initial pose of the object, performing one or more operations to update the initial pose to determine a first pose of the object based on the first image, a first rendered image of the object in the initial pose, and one or more transformer encoders.