ika-rwth-aachen/Cam2BEV
TensorFlow Implementation for Computing a Semantically Segmented Bird's Eye View (BEV) Image Given the Images of Multiple Vehicle-Mounted Cameras.
This project helps automotive engineers and researchers transform multiple camera views from a vehicle into a single, comprehensive bird's-eye view (BEV) image. It takes raw camera images and outputs a semantically segmented BEV image, highlighting elements like roads, vehicles, and pedestrians, even predicting occluded areas. This is crucial for developing and testing advanced driver assistance systems and autonomous driving functionalities.
780 stars. No commits in the last 6 months.
Use this if you need to understand a vehicle's surroundings from a top-down perspective using multiple camera feeds for automated driving research or development.
Not ideal if your application doesn't involve vehicle-mounted cameras, requires real-time processing on very constrained hardware, or you need highly precise distance measurements for non-flat surfaces without semantic segmentation.
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780
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124
Language
Python
License
MIT
Category
Last pushed
May 17, 2025
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