3D Reconstruction Using Complementary View Selection and Fusion
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Solution Overview
Problem
Current 3D reconstruction methods face challenges in generating high-quality 3D objects from 2D images due to limitations in computer resources and time, as they require numerous 2D images to capture all features of a target object effectively.
Innovation Solution
A method that selects a complementary view based on the reconstruction quality of the original 3D object, obtaining a complementary 2D image, and fusing it with the original 3D object to enhance reconstruction quality using fewer 2D images, employing neural networks for regression analysis and voxel representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If more 2D images are used for 3D reconstruction, then the reconstruction quality improves, but the computer resource consumption and time increase
Solution Approach 1:
The patent extracts and utilizes depth information from existing 2D images through depth estimation algorithms. Instead of requiring multiple 2D images, the system extracts depth maps from a single or few images, thereby reducing the number of images needed while maintaining reconstruction quality and decreasing processing time
Solution Approach 2:
The patent transforms 2D image data into 3D spatial representation by estimating depth information. This dimensionality change allows the system to reconstruct 3D objects from fewer 2D images by inferring the third dimension (depth) through neural networks and depth estimation techniques, thus improving efficiency without sacrificing reconstruction quality
2Manufacturing precision
If more 2D images are used for 3D reconstruction, then the reconstruction quality improves, but the computer resource consumption increases
Solution Approach 1:
The system extracts depth information from existing 2D images using depth estimation algorithms rather than processing multiple full-resolution 2D images. This extraction approach reduces computational load and memory requirements while maintaining reconstruction quality
Solution Approach 2:
The patent uses neural networks to generate synthetic depth maps and 3D representations that copy essential geometric information from 2D images. This copying mechanism allows the system to work with compressed or fewer image inputs while preserving the necessary structural information for accurate 3D reconstruction
3Loss of time
If fewer 2D images are used for 3D reconstruction, then the processing time decreases, but the reconstruction quality deteriorates
Solution Approach 1:
The patent replaces traditional multi-image geometric reconstruction methods with neural network-based depth estimation. This substitution allows the system to achieve accurate depth information and high-quality 3D reconstruction from fewer images by using learned patterns rather than mechanical multi-view geometry, thereby reducing processing time without sacrificing quality
4Productivity
If a single 2D image is used for 3D reconstruction, then the processing efficiency improves, but the completeness of object features is insufficient
Solution Approach 1:
The patent performs preliminary depth estimation and 3D shape prediction from a single 2D image before final reconstruction. This preliminary action allows the system to generate an initial 3D model that captures essential object features, which can then be refined if needed, thereby maintaining efficiency while preserving feature completeness
Solution Approach 2:
The system introduces depth maps as an intermediary representation between the single 2D image and the final 3D reconstruction. These depth maps serve as mediators that encode geometric information, allowing the system to recover complete object features from a single image without requiring multiple input images, thus maintaining both efficiency and information completeness
Data Source
AI summary
A method, device, computer system and computer readable storage medium for 3D reconstruction are provided. The method comprises: performing a 3D reconstruction of an original 2D image of a target object to generate an original 3D object corresponding to the original 2D image; selecting a complementary view of the target object from candidate views based on a reconstruction quality of the original 3D object at the candidate views; obtaining a complementary 2D image of the target object based on the complementary view; performing a 3D reconstruction of the complementary 2D image to generate a complementary 3D object corresponding to the complementary 2D image; and fusing the original 3D object and the complementary 3D object to obtain a 3D reconstruction result of the target object.