Single-Image 3D Reconstruction Using Radiance Field Inference
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Solution Overview
Problem
Existing 3D reconstruction systems face challenges in generating accurate 3D representations from limited or scarce RGB or RGB-D data, often requiring costly and technically challenging full 3D data capture, and struggle to make inferences beyond visual data captured by sensors or cameras.
Innovation Solution
A 3D generation system infers the hidden 3D structure of objects from a single RGB image using a generative model, incorporating appearance and foreground mask constraints to generate a radiance field that matches the input image view and allows optimization of other viewpoints, utilizing neural networks to encode 3D scenes as volumetric functions and predict radiance and density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple 2D images from different viewpoints are used for 3D reconstruction, then the accuracy of 3D representation is improved, but the complexity of data capture and processing increases
Solution Approach 1:
The patent transforms the problem from capturing multiple 2D images in spatial dimensions to inferring 3D structure from a single 2D image by adding the dimension of computational inference. The system uses a trained neural network model to infer hidden 3D structures, effectively converting a multi-view geometric problem into a single-image deep learning inference problem, thereby reducing capture complexity while maintaining reconstruction accuracy
Solution Approach 2:
The system performs preliminary training of the neural network model using datasets with multiple viewpoints before deployment. This preliminary action pre-learns the mapping between single 2D images and 3D structures, allowing the system to later infer 3D representations from single images without requiring actual multi-view capture during operation, thus resolving the contradiction between accuracy and capture complexity
2Loss of information
If full 3D data capture is performed, then complete 3D information is obtained, but the cost and technical difficulty increase significantly
Solution Approach 1:
The system creates a computational copy of the 3D reconstruction process through a trained neural network model. Instead of physically capturing complete 3D data through complex multi-view or depth sensing systems, the model learns to generate accurate 3D representations by copying the essential structural information from training data, inferring the remaining details through learned patterns, thereby reducing information loss without increasing capture complexity
Solution Approach 2:
The patent introduces a trained neural network model as an intermediary between the single 2D input image and the final 3D reconstruction. This intermediary performs the complex inference work, filling in missing 3D information based on learned patterns from training data, thereby achieving complete 3D information recovery without requiring complex capture systems that would otherwise be needed to directly obtain all 3D data
3Ease of operation
If a single 2D image is used for 3D reconstruction, then the ease of operation is improved, but the ability to infer hidden 3D structure deteriorates
Solution Approach 1:
The system fundamentally changes the parameter space by training the neural network to operate in a high-dimensional latent space that encodes 3D structural information. The model learns to map from the 2D image parameter space to a 3D representation parameter space, effectively compensating for the information loss by transforming the problem into a different parameter domain where the inference can be performed through learned relationships rather than direct observation
Data Source
AI summary
Embodiments described herein provide a 3D generation system from a single RGB image of an object by inferring the hidden 3D structure of objects based on 2D priors learnt by a generative model. Specifically, the 3D generation system may reconstruct the 3D structure of an object from an input of a single RGB image and optionally an associated depth estimate. For example, a radiance field is formulated to depict the input image in one viewpoint of the target 3D object, based on which other viewpoints of the 3D object can be inferred. Based on the visible surface depicted by the input image, points between the reference camera and the surface are assigned with zero density, and points on the surface are assigned with high density and color equal to the corresponding pixel in the input image.