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SAM Graph
SAM-Graph
S
SAM Graph
AI image detection and recognitionAI 3D tools#3D Instance Segmentation#Deep Learning#Graph Neural Networks#Multi-View ImagesStandard PicksOpen Source
Overview :
SAM-guided Graph Cut for 3D Instance Segmentation is a deep learning approach utilizing 3D geometry and multi-view image information for 3D instance segmentation. By employing a 3D-to-2D query framework, this method effectively leverages 2D segmentation models to perform 3D instance segmentation, constructing a superpoint graph through graph cut problems and training via graph neural networks to achieve robust segmentation performance across diverse scene types.
Target Users :
This technology is suitable for fields requiring 3D instance segmentation, such as autonomous driving, robotic navigation, and augmented reality, particularly in applications that involve complex scenes and lack high-diversity 3D annotation data.
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Use Cases
In autonomous driving, perform 3D instance segmentation of the surrounding environment to identify and track vehicles and pedestrians.
In robotic navigation, execute 3D instance segmentation of indoor environments for precise path planning.
In augmented reality, conduct 3D instance segmentation of real-world scenes to ensure natural integration of virtual objects with the real world.
Features
Utilize 3D geometry and multi-view image information for instance segmentation
3D-to-2D query framework to enhance scene generalization capabilities
Graph cut problem construction to optimize segmentation results
Graph neural network training based on 2D segmentation models
Performance validation on ScanNet, ScanNet++, and KITTI-360 datasets
Achieve robust segmentation for diverse scene types
How to Use
Step 1: Preprocess 3D point cloud data and extract superpoints from the scene.
Step 2: Utilize a 2D segmentation model to segment multi-view images and obtain node features.
Step 3: Calculate edge weights based on multi-view segmentation results and construct a superpoint graph.
Step 4: Train a graph neural network using pseudo 3D labels.
Step 5: Apply a graph cut algorithm on the superpoint graph to achieve 3D instance segmentation.
Step 6: Validate model performance on different datasets and adjust parameters to fit various scenarios.
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