computer-vision-19
3D Semantic Segmentation
3260 papers • 126 benchmarks • 313 datasets
3D Semantic Segmentation is a computer vision task that involves dividing a 3D point cloud or 3D mesh into semantically meaningful parts or regions. The goal of 3D semantic segmentation is to identify and label different objects and parts within a 3D scene, which can be used for applications such as robotics, autonomous driving, and augmented reality.
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Benchmarks
These leaderboards are used to track progress in 3d-semantic-segmentation-19
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SemanticKITTI
SemanticKITTI
ScanNet200
ScanNet200
SensatUrban
SensatUrban
Libraries
iUse these libraries to find 3d-semantic-segmentation-19 models and implementations
12 papers 859
Datasets
No datasets available.
