Camera Parameter Estimation Using Disparity and Signed Defocus
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Solution Overview
Problem
Existing computer vision models lack depth of field and blur estimation, leading to unrealistic insertion of objects in images, as they do not account for the camera and lens parameters, resulting in unwanted effects.
Innovation Solution
A set of neural networks, including a defocus network and a disparity network, are used to generate pixel-wise depth and disparity estimates, which are then used to estimate lens parameters for a virtual camera, allowing accurate insertion and movement of objects with realistic depth and blur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing computer vision models are used for image generation, then the processing speed is fast, but the depth of field and blur estimation are missing resulting in unrealistic images
Solution Approach 1:
The system segments the depth estimation task by introducing a defocus map estimation module that operates independently from the main image generation model. This module processes the generated image to estimate depth information, allowing the main model to remain relatively simple while adding depth capability through a separate, specialized component.
Solution Approach 2:
A defocus map is introduced as an intermediary element between the image generation model and the final output. This defocus map serves as a mediator that carries depth information, enabling the system to generate images with realistic depth of field and blur effects without fundamentally redesigning the entire generation pipeline.
2Reliability
If objects are inserted into images without camera parameter matching, then the editing process is simple, but unwanted effects occur reducing realism
Solution Approach 1:
The system performs preliminary action by automatically estimating camera parameters (focal length, aperture, focus distance) from the input image before object insertion. This pre-computation of lens parameters ensures that any subsequently inserted objects can be rendered with matching depth of field and blur effects, eliminating the need for manual parameter adjustment and preventing realism issues.
Solution Approach 2:
The system changes parameters by dynamically adjusting the depth of field and blur parameters of inserted objects based on the estimated camera parameters from the original image. This automatic parameter adaptation ensures that inserted objects blend realistically with the background, maintaining consistent optical properties without requiring complex manual tuning.
Data Source
AI summary
In various examples, a set of camera parameters associated with an input image are determined based on a disparity map and a signed defocus map. For example, a disparity model generates the disparity map indicating disparity values associated with pixels of the input image and a defocus model generates a signed defocus map indicating blur values associated with the pixels of the input image.