Semantic Segmentation for Depth Estimation Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital imaging technologies face challenges in generating robust depth and disparity estimates, particularly in scenes with similar colored foreground and background objects or those with multiple colors and textures, where traditional color image-based regularization techniques fail to accurately separate depth planes and can result in noisy or unnatural-looking synthetic shallow depth of field images.
Innovation Solution
The use of semantic segmentation information in combination with color information within a joint optimization framework that incorporates data and regularization terms, allowing for weighted importance of segmentation masks and confidence values to refine disparity and depth maps, thereby improving the accuracy of depth and disparity estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional color image-based regularization techniques are used for depth and disparity estimation, then the process is simple and computationally efficient, but the accuracy deteriorates in scenes with similar colored foreground and background objects or objects with multiple colors and textures
Solution Approach 1:
The patent combines color image-based regularization with semantic segmentation information in a joint optimization framework. The segmentation masks provide additional structural constraints that complement the color-based regularization, enabling accurate depth estimation in challenging scenes where color alone is insufficient. This merging of multiple information sources resolves the accuracy limitation while managing complexity through integrated processing.
Solution Approach 2:
The patent creates a composite regularization approach by combining two different types of information: color image data and semantic segmentation masks. This composite regularization term leverages the strengths of both sources - the color information provides smoothness constraints while the segmentation provides object boundary awareness, together achieving superior depth estimation accuracy in complex scenes.
2Reliability
If semantic segmentation information is integrated into depth and disparity estimation, then the robustness improves across various image capture scenarios, but the computational complexity increases
Solution Approach 1:
The patent applies segmentation to divide the image into distinct semantic regions (foreground objects, background, etc.) before performing depth estimation. This segmentation allows the algorithm to apply different regularization strengths to different regions, improving robustness by understanding scene structure while managing computational complexity through region-based processing rather than pixel-by-pixel analysis.
Solution Approach 2:
The patent implements local quality by applying different regularization weights to different spatial regions based on segmentation information. Foreground regions receive different treatment than background regions, allowing the algorithm to adapt to local scene characteristics. This local adaptation improves reliability across diverse scenarios while avoiding the need for a completely different algorithm for each scenario.
3Productivity
If color image-based regularization is used, then the processing is computationally efficient, but depth bleeding occurs across object boundaries in synthetic shallow depth of field images
Solution Approach 1:
The patent performs semantic segmentation as a preliminary step before depth estimation and synthetic SDOF generation. By pre-identifying object boundaries and semantic regions, the algorithm can preserve sharp edges during the subsequent depth-based blurring process. This preliminary action prevents depth bleeding across object boundaries while maintaining processing efficiency through a single-pass optimization framework.
Data Source
AI summary
This disclosure relates to techniques for generating robust depth estimations for captured images using semantic segmentation. Semantic segmentation may be defined as a process of creating a mask over an image, wherein pixels are segmented into a predefined set of semantic classes. Such segmentations may be binary (e.g., a ‘person pixel’ or a ‘non-person pixel’) or multi-class (e.g., a pixel may be labelled as: ‘person,’‘dog,’‘cat,’ etc.). As semantic segmentation techniques grow in accuracy and adoption, it is becoming increasingly important to develop methods of utilizing such segmentations and developing flexible techniques for integrating segmentation information into existing computer vision applications, such as depth and/or disparity estimation, to yield improved results in a wide range of image capture scenarios. In some embodiments, an optimization framework may be employed to optimize a camera device's initial scene depth/disparity estimates that employs both semantic segmentation and color regularization in a robust fashion.