3D Camera Alignment with 2D Backgrounds via Horizon Detection
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Solution Overview
Problem
Conventional 3D composition software requires manual rotation of 3D objects and cameras to align them with 2D background images, which is tedious and time-consuming, as users need to estimate appropriate alignments by hand.
Innovation Solution
An automated image analysis is performed to detect horizons and vanishing points in the 2D background image, with classifiers trained to predict the accuracy of this analysis, enabling automatic alignment of the 3D camera with the image by generating target parameters for transformation, such as field of view, orientation matrix, and yaw adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual rotation of 3D objects and cameras is used to align with 2D background images, then users can achieve alignment, but the process is tedious and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical rotation process with an automated image analysis system that detects horizons and vanishing points algorithmically. The system uses computer vision techniques to automatically determine camera orientation and alignment parameters, eliminating the need for manual trial-and-error rotation while achieving precise alignment results.
Solution Approach 2:
The system performs self-service by automatically analyzing the 2D background image to extract alignment information without requiring user intervention. The automated image analysis detects horizons and vanishing points, calculates camera orientation, and applies transformations to align 3D objects automatically, making the system self-sufficient and eliminating manual labor.
2Measurement precision
If automated image analysis is performed to detect horizons and vanishing points, then alignment accuracy is improved, but the complexity of the system increases
Solution Approach 1:
The patent introduces an intermediary classification system that acts as a mediator between the automated image analysis and the alignment process. The classifier evaluates the confidence level of detected horizons and vanishing points, and only enables automatic alignment when confidence exceeds a threshold. This intermediary layer ensures high alignment accuracy by filtering out low-quality detections while managing system complexity through structured decision-making.
3Productivity
If automatic 3D camera alignment is enabled based on classifier confidence, then alignment efficiency is improved, but the reliability of inaccurate alignment increases
Solution Approach 1:
The system implements feedback through a confidence-based classification mechanism that continuously monitors the quality of image analysis results. The classifier provides feedback on the reliability of detected horizons and vanishing points, and the system adjusts its behavior accordingly by enabling or disabling automatic alignment based on confidence thresholds. This feedback loop ensures high productivity by enabling automatic alignment only when reliability conditions are met.
Data Source
AI summary
Embodiments disclosed herein provide systems, methods, and computer storage media for automatically aligning a 3D camera with a 2D background image. An automated image analysis can be performed on the 2D background image, and a classifier can predict whether the automated image analysis is accurate within a selected confidence level. As such, a feature can be enabled that allows a user to automatically align the 3D camera with the 2D background image. For example, where the automated analysis detects a horizon and one or more vanishing points from the background image, the 3D camera can be automatically transformed to align with the detected horizon and to point at a detected horizon-located vanishing point. In some embodiments, 3D objects in a 3D scene can be pivoted and the 3D camera dollied forward or backwards to reduce changes to the framing of the 3D composition resulting from the 3D camera transformation.