SAM-Graph
An innovative approach to 3D instance segmentation
Product Details
SAM-guided Graph Cut for 3D Instance Segmentation is a deep learning method that utilizes 3D geometry and multi-view image information for 3D instance segmentation. This method effectively utilizes 2D segmentation models for 3D instance segmentation through a 3D to 2D query framework, constructs superpoint graphs through graph cut problems, and achieves robust segmentation performance for different types of scenes through graph neural network training.
Main Features
How to Use
Target Users
This technology is suitable for fields that require 3D instance segmentation, such as autonomous driving, robot navigation, augmented reality, etc., and is especially suitable for application scenarios that need to process complex scenes and lack high-diversity 3D annotation data.
Examples
In autonomous driving, 3D instance segmentation of the surrounding environment is performed to identify and track vehicles and pedestrians.
In robot navigation, 3D instance segmentation is performed on the indoor environment to achieve accurate path planning.
In augmented reality, 3D instance segmentation is performed on real-world scenes to achieve natural integration of virtual objects and the real world.
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