3D Gaussian Primitive Reconstruction from Sparse Image Patches
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Solution Overview
Problem
Conventional three-dimensional reconstruction techniques are computationally inefficient and require large numbers of input digital images, which is impractical for typical users lacking the resources or time to capture hundreds or thousands of images.
Innovation Solution
A reconstruction system that uses a sparse input of two digital images from different angles, segmented into patches, processed by a Transformer model with transformer blocks to generate three-dimensional Gaussian primitives, which are merged to form an accurate three-dimensional reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional three-dimensional reconstruction techniques are used, then the reconstruction accuracy can be maintained, but the computational efficiency deteriorates and large numbers of input images are required
Solution Approach 1:
The patent segments the input images into multiple patches and processes them independently through the neural network. This segmentation allows the system to handle sparse input images more efficiently by focusing computational resources on local regions rather than processing entire high-resolution images, thereby improving computational efficiency while maintaining reconstruction accuracy with fewer input images.
Solution Approach 2:
The patent transforms the problem from traditional mesh-based 3D reconstruction to Gaussian primitive-based reconstruction. By representing 3D surfaces as collections of Gaussian primitives in a five-dimensional space (x, y, z, sigma, intensity), the system achieves higher computational efficiency and can work effectively with sparse input images, resolving the contradiction between productivity and quantity of input images.
2Reliability
If conventional three-dimensional reconstruction techniques are used, then the reconstruction can be formed, but the processing time increases significantly
Solution Approach 1:
The patent replaces traditional mechanical 3D reconstruction methods (such as structure from motion and manual mesh processing) with a neural network-based system. The Transformer-based neural network directly predicts Gaussian primitives from image patches, eliminating time-consuming intermediate steps and significantly reducing processing time while maintaining or improving reconstruction accuracy.
Solution Approach 2:
The patent changes the fundamental parameters of representation from mesh vertices and faces to Gaussian primitive parameters (mean, covariance, intensity). This parameter transformation enables the system to achieve accurate reconstructions with fewer computational operations, thereby reducing processing time while maintaining reliability.
3Ease of operation
If sparse input images are used, then the user burden is reduced, but the reconstruction quality may deteriorate
Solution Approach 1:
The patent introduces Gaussian primitives as an intermediary representation between sparse input images and the final 3D reconstruction. The neural network learns to infer accurate Gaussian primitives from limited image data, and these primitives serve as a compact, information-rich intermediate representation that enables high-quality reconstruction even from sparse inputs, thus maintaining manufacturing precision while improving ease of operation.
Solution Approach 2:
The patent creates a compact copy of the 3D scene in the form of Gaussian primitives, which capture essential geometric and photometric information. This Gaussian representation serves as an efficient intermediary that can be generated from sparse inputs and then used to synthesize high-quality views, effectively copying the essential features of the scene without requiring complete input coverage.
Data Source
AI summary
In implementation of techniques for three-dimensional reconstructions based on Gaussian primitives, a computing device implements a reconstruction system to receive a first digital image depicting an object from a first angle and a second digital image depicting the object from a second angle. The reconstruction system segments the first digital image and the second digital image into patches. The reconstruction system then generates, using a machine learning model, three-dimensional Gaussian primitives that predict parameters of points of the object in a three-dimensional space that correspond on a per-pixel basis to pixels of the patches. The reconstruction system then forms a three-dimensional reconstruction of the object for display in a user interface by merging the three-dimensional Gaussian primitives.