3D Mesh Reconstruction from 2D Images via Automated Depth Estimation
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Solution Overview
Problem
Current methods for generating three-dimensional (3D) models require significant user input or rely on costly depth cameras, often producing pseudo-3D outputs that are static and lack context, failing to provide fully interactive and dynamically viewable 3D representations.
Innovation Solution
A system and method that reconstructs 3D meshes from 2D image data by dynamically selecting computing devices based on user demand, processing 2D datasets to classify objects, reconstruct depth values, and filter geometric components, while incorporating texture and prior reconstruction results to create fully interactive and viewable 3D models using conventional imaging technology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If user-driven approaches are used to generate 3D models, then model accuracy and control are improved, but operational complexity and cost increase significantly
Solution Approach 1:
The system automatically performs 3D reconstruction by processing uploaded 2D images without requiring user guidance during the reconstruction process. The automated pipeline includes image processing, depth estimation, mesh generation, and texturing, all executed without user intervention, enabling ordinary users to obtain 3D models simply by providing source images.
Solution Approach 2:
The patent replaces manual user input mechanisms with automated computational algorithms. Instead of requiring users to manually control camera movements or provide geometric parameters, the system uses computer vision algorithms to automatically extract 3D information from 2D images, substituting mechanical user operations with automated digital processing.
2Manufacturing precision
If depth cameras are used to capture physical scenes, then 3D reconstruction capability is improved, but device cost and complexity increase
Solution Approach 1:
The system creates a virtual 3D copy from 2D images rather than requiring direct optical capture with depth cameras. By processing multiple 2D images through computational algorithms, the system reconstructs depth information and 3D geometry, effectively copying the 3D scene properties from 2D representations without needing specialized depth-capturing hardware.
Solution Approach 2:
The patent employs conventional, inexpensive 2D imaging devices (standard cameras) instead of expensive depth cameras. The system accepts standard image data from affordable cameras and transforms it into 3D representations through software processing, eliminating the need for costly specialized hardware while achieving comparable or superior reconstruction results.
3Device complexity
If conventional 2D imaging is used, then device cost is reduced, but 3D model completeness and interactivity are worsened
Solution Approach 1:
The system transforms 2D image data into 3D model output by adding the depth dimension through computational reconstruction. By processing multiple 2D images and synthesizing depth information, the system creates fully 3D models that can be rotated, viewed from multiple angles, and interacted with in three-dimensional space, effectively adding a third dimension to conventional 2D imaging.
4Ease of operation
If automated 3D model generation is implemented, then operational complexity is reduced, but measurement precision and model accuracy are worsened
Solution Approach 1:
The automated reconstruction pipeline incorporates feedback mechanisms where the system processes 2D images, generates preliminary 3D models, and can refine results based on image analysis. The system uses depth estimation algorithms that continuously adjust and improve accuracy by analyzing image overlaps, object classifications, and geometric consistency across multiple views, enabling automated generation to achieve high precision.
Data Source
AI summary
Implementations are disclosed herein that relate to three-dimensional scene reconstruction. An example provides a computing device comprising a logic machine and a storage machine holding instructions executable by the logic machine to receive a two-dimensional dataset comprising two-dimensional image data of a physical scene, normalize the two-dimensional dataset, classify one or more objects in the two-dimensional dataset, reconstruct one or more depth values associated with the two-dimensional dataset, and construct a three-dimensional mesh of the physical scene based on the one or more classified objects and the one or more depth values. The instructions may be further executable to filter the three-dimensional mesh, and output the three-dimensional mesh.