3D Shape Estimation from Single 2D Image
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Solution Overview
Problem
Current methods for estimating the 3D shape of objects from 2D images, particularly faces, require specialized equipment and limited measurement environments, making it impossible to estimate 3D shapes from single images without manual feature point designation and restricting the use of images taken in specific conditions.
Innovation Solution
A 3D shape estimation system that calculates the illumination basis and relative shape function from a single 2D image using learning data, automatically identifies feature points, and converts these into absolute 3D shape data without needing special measurement apparatuses, allowing for the generation of images with different illumination or orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized measurement equipment (stereo cameras, pattern irradiators) is used to estimate 3D shape, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a single 2D image as a copy or representation of the object, and through computational algorithms (illumination basis calculation, relative shape function estimation), reconstructs 3D shape information without needing physical 3D measurement devices. This replaces complex measurement apparatus with a simple camera and computational process.
Solution Approach 2:
The patent replaces mechanical/optical measurement systems (stereo cameras, pattern irradiators) with a computational system that processes a single 2D image. The mechanical complexity of multiple cameras and irradiators is substituted by algorithmic processing of illumination basis and shape functions.
2Measurement precision
If specialized measurement equipment is used, then measurement precision is improved, but ease of operation deteriorates due to limited measurement environment
Solution Approach 1:
The patent makes a single ordinary camera perform the function of multiple specialized measurement devices. By calculating illumination basis and relative shape functions from a single image, the system achieves 3D shape estimation capability that traditionally required stereo cameras, pattern irradiators, and multiple sensors, thus improving ease of operation and environmental flexibility.
Solution Approach 2:
The system uses the single 2D image itself to provide all necessary information for 3D reconstruction. The image contains illumination and shape information that the algorithm extracts through illumination basis calculation, without needing external measurement devices or controlled environments.
3Measurement precision
If manual feature point designation is required for 3D shape estimation, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent makes the system self-sufficient by automatically estimating relative shape functions and feature points from the image data itself. The algorithm calculates illumination basis and shape derivatives without requiring manual feature point designation, thereby maintaining measurement precision while dramatically improving ease of operation.
Solution Approach 2:
The patent performs preliminary calculation of illumination basis and relative shape functions from the single image before final 3D shape reconstruction. This preliminary processing automatically identifies feature information and shape characteristics, eliminating the need for manual feature point designation in subsequent steps.
4Measurement precision
If multiple images under different illumination are used, then 3D shape estimation accuracy is improved, but productivity deteriorates due to multiple measurement requirements
Solution Approach 1:
The patent segments the information extraction process by calculating illumination basis and relative shape functions separately from the final 3D reconstruction. This allows the system to extract all necessary shape information from a single image through computational segmentation of illumination and geometric components, achieving multi-illumination accuracy without requiring multiple physical images.
Solution Approach 2:
The patent transitions from the temporal dimension (multiple images taken at different times/illumination conditions) to the computational dimension (single image processed through illumination basis calculation). By operating in the dimension of computational analysis rather than physical measurement, the system achieves accurate 3D shape estimation from a single image, dramatically improving productivity.
Data Source
AI summary
A 3D shape estimation system has a storage device, a relative shape analysis module, a feature point location search module and an absolute shape analysis module. The storage device stores first and second learning data which represent illumination bases and 3D shapes of objects, respectively. The relative shape analysis module calculates an “illumination basis” of an object based on a 2D image of the object and the first learning data, calculates a “relative shape function” that is partial differential of a “shape function” indicating a 3D shape of the object from the illumination basis, and outputs a relative shape data indicating the relative shape function. The feature point location search module extracts a plurality of feature points from the input 2D face image based on the 2D image and the relative shape data, and outputs a feature point location data indicating locations of the feature points. The absolute shape analysis module receives the relative shape data and the feature point location data, converts the relative shape function into the shape function by referring to the second learning data and the locations of the feature points, and outputs a 3D absolute shape data indicating the shape function.