Segmentation Network for Depth Camera Pose Registration
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Solution Overview
Problem
Conventional depth cameras face challenges in accurately registering two-dimensional depth images due to background structures, which hinder the identification of objects of interest and precise camera pose determination, especially in cluttered environments.
Innovation Solution
A system comprising a trained segmentation network and a descriptor generation network is used to segment images into foreground and background, generating a compact representation that focuses on foreground elements, enabling efficient camera pose identification and registration by associating these representations with corresponding camera poses in a database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional depth cameras are used to acquire two-dimensional depth images, then the ability to capture scene data is provided, but background structures hinder accurate object identification and registration
Solution Approach 1:
The image processing system segments the two-dimensional depth image into multiple regions including foreground object regions and background regions. By separating the object of interest from background structures through segmentation, the system eliminates background interference and improves object identification accuracy in cluttered environments.
Solution Approach 2:
The system extracts the foreground object from the complete scene by identifying and isolating object-specific features while removing background structures. This extraction process creates a cleaned representation of the object that can be accurately registered without background interference.
2Manufacturing precision
If the entire two-dimensional depth image is used for registration, then complete scene information is available, but processing time and computational complexity increase
Solution Approach 1:
The system segments the image to identify only the relevant foreground object regions, excluding background areas from further processing. This segmentation enables the system to process only the necessary portions of the image, reducing computational time while maintaining registration accuracy.
Solution Approach 2:
The system extracts key object features from the complete scene and uses only these extracted features for registration processing. By working with extracted object representations rather than the entire image, the system achieves accurate registration with significantly reduced processing time.
3Measurement precision
If background structures are included in image processing, then complete environmental context is preserved, but camera pose determination accuracy decreases
Solution Approach 1:
The system segments the two-dimensional depth image to distinguish between foreground object regions and background environmental regions. By separating these regions, the system can process object-specific features for accurate camera pose determination while maintaining the option to preserve or discard background information as needed.
Solution Approach 2:
The system applies different processing qualities to different regions of the image: high-detail processing is applied to foreground object regions for accurate pose determination, while background regions can be processed with lower detail or excluded entirely, optimizing the balance between accuracy and information retention.
Data Source
AI summary
A system and method includes creation of a combined network comprising an image segmentation network and an image representation network, the combined network to generate an image descriptor based on an input query image, training of the combined network based on a plurality of first images and a segmentation mask associated with each of the plurality of first images, reception of a first input query image, use of the combined network to generate an image descriptor based on the first input query image, determination of a matching image descriptor from a plurality of stored image descriptors, determination of a camera pose associated with the matching image descriptor, registration of the first input query image with image data based on the determined camera pose, generation of a composite image based on the registered first input query image and image data, and presentation of the composite image.