3D Object Position Estimation Using Virtual Camera Views
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Solution Overview
Problem
Conventional methods for estimating three-dimensional positions of objects suffer from reduced accuracy, particularly in determining the distance to an object, and existing gaze estimation systems lack precision in object positioning.
Innovation Solution
A method involving the use of a physical camera to capture an image, detect target objects, generate virtual cameras with predetermined calibration parameters, and apply a trained model to estimate three-dimensional positions using a probability distribution, constrained by transformation functions to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to estimate three-dimensional positions from single images, then the process is simple, but the accuracy of distance estimation deteriorates
Solution Approach 1:
The patent creates virtual camera images as copies of the real camera's perspective, then generates additional virtual views from different positions. These synthetic images serve as substitutes for actual physical cameras, enabling multi-perspective analysis without adding physical device complexity. The virtual camera system replicates the optical projection process computationally to achieve accurate 3D position estimation.
Solution Approach 2:
The patent transitions from analyzing a single two-dimensional image to synthesizing and comparing multiple virtual images from different spatial perspectives. By adding the dimension of virtual camera positions and orientations, the system can triangulate 3D positions more accurately. This dimensional expansion occurs in the computational domain rather than requiring additional physical sensors.
2Measurement precision
If multiple physical cameras are used to improve three-dimensional position estimation accuracy, then the accuracy improves, but the device complexity and cost increase
Solution Approach 1:
Instead of deploying multiple physical cameras, the patent creates virtual camera copies through computational processing of a single real camera's image. These virtual cameras are synthesized with different intrinsic and extrinsic parameters, providing multi-perspective views without the hardware complexity, cost, and synchronization requirements of multiple physical devices.
Solution Approach 2:
The patent replaces the mechanical/optical system of multiple physical cameras with a computational system that generates virtual images. The complex mechanical setup of mounting, aligning, and calibrating multiple cameras is substituted by algorithms that mathematically transform a single image into multiple virtual perspectives, achieving the same measurement goal with simpler hardware.
3Measurement precision
If virtual cameras with different calibration parameters are used, then the accuracy of three-dimensional position estimation improves, but the computational complexity increases
Solution Approach 1:
The patent segments the computational task by dividing it into distinct stages: detecting target objects in the real image, generating virtual camera images with different calibration parameters, estimating 3D positions from each virtual view, and combining results. This segmentation allows for optimized processing at each stage and enables parallel computation of multiple virtual perspectives, reducing overall computational burden.
Solution Approach 2:
The patent performs preliminary detection of target objects in the real camera image before generating virtual views. By identifying objects of interest first, the system can limit subsequent virtual image generation and processing to only those regions containing detected objects, significantly reducing the computational workload compared to processing entire images through multiple virtual cameras.
Data Source
AI summary
The disclosure relates to a method of estimating a three-dimensional position of at least one target object performed by a computer, the method comprising obtaining a first image, detecting at least one target object, generating a set of images depicting subsets of a captured scene, and estimating a three-dimensional position of at least one target object using a probability distribution (P) indicative of a likelihood that the at least one target object is in a particular position (x, y, z) in the scene.