Generative Model Depth Estimation for Occluded Regions
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Solution Overview
Problem
Existing depth estimation techniques struggle to accurately calculate depth when corresponding pixels are occluded or not visible from different viewpoints, leading to inaccurate depth maps, especially around object edges.
Innovation Solution
The use of generative models, such as convolutional neural networks (CNNs) or generative adversarial networks (GANs), to estimate depth by generating a target image from a different viewpoint based on source images, allowing for the identification of depth information even in occluded regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional disparity-based depth estimation methods are used, then depth calculation is straightforward when corresponding pixels are visible, but depth accuracy deteriorates in occlusion zones where corresponding pixels are not visible
Solution Approach 1:
The patent introduces an intermediate target image from a virtual viewpoint as a mediator between source images and depth estimation. This target image, generated by a generative model, provides additional depth cues that bridge the gap in occlusion zones where traditional disparity methods fail, enabling accurate depth estimation even when corresponding pixels are not directly visible
Solution Approach 2:
The patent adds a new dimension to depth estimation by generating a target image from a virtual viewpoint that is neither of the original source viewpoints. This intermediate viewpoint provides additional geometric constraints and depth information that complement the two-view geometry, enabling robust depth estimation in occlusion regions
2Measurement precision
If generative models are used to generate target images from virtual viewpoints, then depth estimation accuracy improves in occlusion zones, but system complexity increases
Solution Approach 1:
The patent replaces complex multi-camera mechanical systems with a generative model that computationally synthesizes the effect of additional viewpoints. Instead of physically deploying multiple cameras to capture images from different angles, the system uses learned generative models to simulate these views, reducing hardware complexity while maintaining depth estimation accuracy
Solution Approach 2:
The patent creates a synthetic copy of the scene from a virtual viewpoint using generative models. This copied target image, generated from source images through learned transformations, provides the necessary depth information without requiring physical duplication of camera hardware, thus managing system complexity
Data Source
AI summary
Systems and methods for depth estimation in accordance with embodiments of the invention are illustrated. One embodiment includes a method for estimating depth from images. The method includes steps for receiving a plurality of source images captured from a plurality of different viewpoints using a processing system configured by an image processing application, generating a target image from a target viewpoint that is different to the viewpoints of the plurality of source images based upon a set of generative model parameters using the processing system configured by the image processing application, and identifying depth information of at least one output image based on the predicted target image using the processing system configured by the image processing application.