3D Gaussian Diffusion for Single-View Geometry Reconstruction
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Solution Overview
Problem
Existing 3D reconstruction methods from single-view images face challenges in accurately encoding high-fidelity 3D information, synthesizing photorealistic views, and ensuring faithful reconstruction, particularly due to issues with 3D geometry inconsistency, time-consuming optimization processes, and reliance on pre-trained models that struggle with real-world adaptability.
Innovation Solution
A method involving progressively denoising randomly initialized 3D Gaussian representations using a diffusion model with continuous image guidance, employing a denoising function and diffusion process to refine the representations, and utilizing image-guided sampling to minimize differences with the input image, thereby enhancing 3D geometry and texture fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If Score Distillation Sampling (SDS) is used for 3D reconstruction, then photorealistic view synthesis is achieved, but the optimization process becomes time-consuming
Solution Approach 1:
The patent applies pre-trained 2D foundation models and 3D priors before the reconstruction process to guide the optimization from the beginning, rather than starting from random initialization. This preliminary preparation of guidance signals significantly accelerates the convergence speed while maintaining accurate 3D geometry reconstruction.
2Manufacturing precision
If pre-trained image generation models are used, then photorealistic rendering is achieved, but 3D geometry consistency deteriorates due to multi-face problems
Solution Approach 1:
The patent introduces 3D Gaussian representations as an intermediary structure that mediates between 2D image observations and 3D geometry reconstruction. This intermediary enforces 3D consistency constraints while allowing photorealistic texture synthesis, preventing the multi-face problem by ensuring all views are consistent with a single coherent 3D structure.
Solution Approach 2:
The patent changes the parameterization approach by representing 3D geometry as a set of Gaussian ellipsoids with explicit parameters (position, covariance, color, opacity) rather than relying on implicit neural representations. This explicit parameterization makes the 3D structure more controllable and consistent across different views.
3Measurement precision
If explicit 3D representations are used, then accurate geometry is recovered, but photorealistic view synthesis capability is lost
Solution Approach 1:
The patent creates a composite representation by combining explicit 3D Gaussian geometry with implicit neural network-based texture and appearance models. The explicit Gaussians provide accurate geometry and structure, while the neural networks synthesize photorealistic textures and views, achieving both geometric accuracy and visual fidelity simultaneously.
Data Source
AI summary
Methods, devices, and processor-readable media for method for performing a 3D reconstruction from a single view image, comprising progressively denoising a randomly initialized set of 3D-gaussian representations with continuous guidance from an input image.