Joint Depth Estimation and Semantic Segmentation
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Solution Overview
Problem
Conventional depth estimation and semantic labeling techniques are typically performed separately or sequentially, leading to inaccuracies and errors due to their unconnected nature, which limits their effectiveness in providing accurate joint depth and semantic information from a single image.
Innovation Solution
A joint depth estimation and semantic labeling framework that uses machine learning to estimate global and local semantic and depth layouts, merging these to assign accurate semantic labels and depth values to individual pixels in an image, thereby enhancing consistency and accuracy through a coarse-to-fine approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If depth estimation and semantic labeling are performed separately or sequentially using conventional techniques, then the availability of dedicated hardware (stereoscopic cameras, depth sensors) is required, but this increases device complexity and limits accessibility
Solution Approach 1:
The patent combines depth estimation and semantic labeling into a single unified neural network framework that processes a single RGB image to produce both depth maps and semantic segmentations simultaneously, eliminating the need for separate dedicated depth sensors or stereoscopic camera systems
Solution Approach 2:
The unified neural network performs multiple functions (depth estimation and semantic labeling) from a single image input, making the system versatile and accessible without requiring specialized hardware configurations
2Ease of manufacture
If depth estimation and semantic labeling are performed separately or sequentially, then different and unrelated techniques can be used, but this leads to propagation of errors from early stages to later stages
Solution Approach 1:
By merging depth estimation and semantic labeling into a single unified neural network that processes the image simultaneously, the system eliminates error propagation between separate processing stages while maintaining flexibility in technique selection through the unified architecture
Solution Approach 2:
The unified framework allows mutual refinement between depth and semantic predictions through shared feature representations and joint loss functions, where each task provides feedback to improve the other's accuracy
3Device complexity
If conventional separate techniques are used for depth estimation and semantic labeling, then the processing can be simpler, but this results in lower accuracy and inconsistency between depth values and semantic labels
Solution Approach 1:
The patent employs a coarse-to-fine segmentation approach where the image is divided into segments for local semantic and depth layout estimation, guided by global layouts, achieving high accuracy while maintaining manageable processing complexity through hierarchical processing
Solution Approach 2:
The unified neural network processes both depth and semantic information in a joint multi-dimensional output space, allowing simultaneous optimization of both tasks and ensuring consistency between depth values and semantic labels that separate techniques cannot achieve
Data Source
AI summary
Joint depth estimation and semantic labeling techniques usable for processing of a single image are described. In one or more implementations, global semantic and depth layouts are estimated of a scene of the image through machine learning by the one or more computing devices. Local semantic and depth layouts are also estimated for respective ones of a plurality of segments of the scene of the image through machine learning by the one or more computing devices. The estimated global semantic and depth layouts are merged with the local semantic and depth layouts by the one or more computing devices to semantically label and assign a depth value to individual pixels in the image.