Image Depth Estimation Using Point Clouds and Edge Cues
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Solution Overview
Problem
Existing methods for optimizing depth information in environmental images using coordinate calculations result in relatively low accuracy and reliability, leading to incomplete or incorrect depth information.
Innovation Solution
A method that combines environmental images and point cloud data to determine depth information and feature relationships between pixel points, optimizing depth information through a trained image depth estimation model to enhance accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If coordinate calculation method is used to optimize depth information, then the processing method is simple, but the accuracy and reliability of depth information is low
Solution Approach 1:
The patent combines environmental images and point cloud data as multi-modal inputs to the depth estimation model. This merging of different data sources enables the model to leverage both visual appearance information and geometric depth information, significantly improving depth estimation accuracy while maintaining reasonable system complexity through unified model processing
Solution Approach 2:
The patent introduces edge information as an intermediary element that captures boundary characteristics of objects in the environmental image. This edge information serves as a mediator between the input image and depth estimation, providing structural cues that enhance the model's ability to accurately estimate depth, particularly at object boundaries where depth transitions are critical
2Reliability
If coordinate calculation method is used to optimize depth information, then the processing complexity is low, but the reliability of depth information is low
Solution Approach 1:
The patent employs a feedback mechanism where the depth estimation model processes both environmental images and point cloud data, generates depth predictions, and uses loss calculation based on ground truth depth information to adjust model parameters iteratively. This feedback loop during training ensures the model learns reliable depth estimation patterns, improving the reliability of depth information output
Solution Approach 2:
The patent performs preliminary extraction of edge information from environmental images before feeding them into the depth estimation model. This preliminary processing step prepares structurally enhanced input data that contains pre-extracted boundary features, allowing the model to focus on depth estimation rather than learning edge detection from scratch, thereby improving reliability while managing complexity
Data Source
AI summary
A method for training an image depth estimation model. A sample environmental image, sample environmental point cloud data and sample edge information of the sample environmental image are input into a to-be-trained model; initial depth information of each of pixel points in the sample environmental image and a feature relationship between each of the pixel points and a corresponding neighboring pixel point of each of the pixel points are determined through the to-be-trained model, the initial depth information of each of the pixel points is optimized according to the feature relationship to obtain optimized depth information of each of the pixel points, and a parameter of the to-be-trained model is adjusted according to the optimized depth information to obtain the image depth estimation model.