Single-Image 3D Reconstruction Using Radiance Field Inference
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Solution Overview
Problem
Existing 3D reconstruction systems struggle to accurately generate 3D models from limited or scarce RGB or RGB-D data, often requiring costly and technically challenging full 3D data capture, and often fail to make inferences beyond visual data captured by sensors.
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 traditional multi-view 3D reconstruction methods are used, then measurement precision is improved, but device complexity and data acquisition difficulty increase significantly
Solution Approach 1:
The patent segments the complex multi-view reconstruction process into a simplified single-view approach by introducing auxiliary 2D images and text descriptions. This breaks down the difficult task of capturing multiple 3D views into manageable components: a single main image plus supplementary 2D representations that can be processed independently and integrated to achieve accurate 3D reconstruction without requiring complex multi-camera systems.
Solution Approach 2:
The patent introduces text descriptions and auxiliary 2D images as intermediary elements that bridge the gap between a single 2D image and accurate 3D reconstruction. These intermediaries carry semantic information about the target object's appearance, structure, and characteristics from different viewpoints, enabling the system to infer 3D properties without directly capturing multiple 3D views, thus reducing device complexity while maintaining measurement precision.
2Manufacturing precision
If full 3D data capture is performed, then reconstruction quality is improved, but cost and technical difficulty increase
Solution Approach 1:
The patent applies partial action by capturing only the essential information needed for 3D reconstruction through a single main image supplemented with auxiliary 2D images and text descriptions, rather than performing complete full 3D data capture. This selective approach focuses on acquiring the minimum necessary data (single view plus semantic auxiliaries) to achieve high reconstruction quality, significantly reducing implementation cost and technical difficulty while maintaining manufacturing precision.
Solution Approach 2:
The patent replaces complex mechanical 3D capture systems (multiple cameras, depth sensors, rotating rigs) with a computational approach using vision-language models. Instead of mechanically capturing 3D data from multiple angles, the system uses AI to infer 3D structure from 2D images and text descriptions, substituting physical measurement infrastructure with intelligent algorithms that process visual and linguistic information to achieve accurate reconstruction at lower cost and technical complexity.
3Ease of operation
If single 2D image input is used, then ease of operation is improved, but information completeness deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-processing the single 2D input image to extract and enhance relevant visual features, then supplementing it with auxiliary 2D images and text descriptions that provide additional semantic information. This preliminary enrichment of the input data before processing ensures that the vision-language model receives comprehensive information about the target object's appearance, structure, and characteristics, compensating for the inherent information loss in single 2D imaging while maintaining ease of operation.
Solution Approach 2:
The patent transitions from 2D spatial dimensions to include semantic dimensions by introducing text descriptions and auxiliary 2D representations. This dimensional expansion adds new information channels (semantic attributes, textual properties, alternative 2D views) that complement the main image, allowing the system to recover 3D structural information that would be lost in a single 2D view by leveraging information from multiple representational dimensions.
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.