gist-ailab/uoais
Codes of paper "Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling", ICRA 2022
This project helps industrial automation and robotics engineers accurately identify and segment objects in complex, cluttered environments, even when they are partially hidden. It takes real-time RGB-D camera feeds (color and depth information) from sensors like Azure Kinect or Intel Realsense and outputs precise outlines of objects, including the parts that are occluded. Robotics engineers can use this to improve robot manipulation, grasping, and scene understanding in factories or warehouses.
149 stars. No commits in the last 6 months.
Use this if you need to enable a robot or an automated system to 'see' and precisely understand the full shape of individual objects in a cluttered scene, even when those objects are partially blocked by others.
Not ideal if you only need to detect objects that are fully visible, or if your application does not involve depth-sensing cameras.
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149
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28
Language
Python
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Last pushed
Jul 23, 2025
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