3D Wireframe Generation Using Gaussian Splatting for Novel Objects
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Solution Overview
Problem
Conventional 3D modeling methods for object pose estimation and tracking are time-intensive and rely on user expertise, limiting their applicability to predefined objects and failing for novel objects without existing models.
Innovation Solution
A processor-implemented method using Gaussian splatting to generate 3D wireframe models from images, involving edge map generation, Gaussian distribution parameter modification, and iterative alignment to create accurate wireframes without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual 3D modeling methods are used, then modeling accuracy can be achieved, but the process is time-intensive and requires user expertise
Solution Approach 1:
The system automatically generates 3D wireframe models by processing images through edge detection, Gaussian distribution fitting, and iterative optimization algorithms. The method eliminates the need for manual user intervention in model creation, allowing the system to self-service the wireframe generation task while maintaining accuracy through computational optimization.
Solution Approach 2:
The patent replaces manual mechanical modeling operations with an automated computational system. Instead of users manually creating 3D models using modeling software, the system uses image processing algorithms, Gaussian splatting techniques, and automated optimization to generate wireframes, substituting human manual work with automated computational processes.
2Reliability
If manually created 3D models are used, then pose estimation can be performed, but the system only works for predefined objects
Solution Approach 1:
The automated wireframe generation system provides universal functionality by being able to process any object that appears in input images. Unlike manual modeling that requires pre-existing templates, this system can generate 3D wireframes for novel objects on-demand, making the pose estimation system adaptable to any object type while maintaining reliability through consistent algorithmic processing.
Solution Approach 2:
The system adapts to different objects by dynamically adjusting parameters during the Gaussian optimization process. The algorithm modifies Gaussian distribution parameters, edge thickness, and optimization criteria based on the specific characteristics of each object in the input images, enabling versatile handling of different object types while maintaining accurate pose estimation.
3Manufacturing precision
If expert users create 3D models, then high-quality models are produced, but the process requires substantial user expertise
Solution Approach 1:
The system performs the complex task of 3D wireframe generation automatically without requiring expert user intervention. The computational algorithms self-manage the complex processes of edge detection, Gaussian fitting, and model optimization, eliminating the need for users to have specialized 3D modeling expertise while maintaining high model quality through algorithmic precision.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved three-dimensional reconstruction using machine learning. In an example method, an image depicting an object is accessed, and an edge map comprising a plurality of edges is generated based on the image. A thickness of each of the plurality of edges in the edge map is modified based on a current stage of the three-dimensional reconstruction. A rendered image depicting a set of Gaussian distributions in a three-dimensional virtual space is generated using Gaussian splatting. One or more parameters of one or more of the set of Gaussian distributions are modified based on comparing the rendered image and the edge map, and after modifying the one or more parameters, a three-dimensional wireframe model of the object is generated based on the set of Gaussian distributions.