darrenjkt/SEE-MTDA
(RA-L 2022) See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation.
This project helps automotive engineers and robotics developers adapt 3D object detection systems to work with various lidar sensors without extensive retraining. It takes raw lidar data from different manufacturers and models, processes it to normalize inconsistencies, and then outputs highly accurate 3D bounding box detections for objects like cars and pedestrians. This is for professionals building autonomous vehicles or advanced robotics systems that rely on precise environmental sensing.
No commits in the last 6 months.
Use this if you need to deploy a 3D object detection system across vehicles or robots equipped with different lidar sensors and want to avoid time-consuming model fine-tuning for each new sensor.
Not ideal if your application uses only one specific lidar sensor type and does not require adaptability across varied lidar hardware.
Stars
51
Forks
8
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 26, 2023
Commits (30d)
0
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