Hello all,
I am trying to have object detection using point clouds. But the constraint is I cannot use machine learning approach because I do not have enough data to train a network and it is not an usual class object found in the pre-trained networks.
I want to proceed with traditional approach. I have come across segmentation and clustering approaches for obtaining the points that represent the desired object in the 3d point cloud. I have also tried RANSAC to get the plane on which the object lies in order to eliminate outlier points.
I am still looking for any other ideas which can serve the purpose. I would be really grateful if I can find some new ideas.
Thank you very much!
I hope you may perceive some clue over https://towardsdatascience.com/lidar-point-cloud-based-3d-object-detection-implementation-with-colab-part-2-of-2-f3ad55c3f38c & https://analyticsindiamag.com/objectron-dataset-tutorial-in-python/
Dear Karishma Patil ,
2D object detection classifies the object category and estimates oriented 2D bounding boxes of physical objects from 3D sensor data.
https://paperswithcode.com/task/3d-object-detection
Regards,
Shafagat
Hi Chandni Patel, look to the following link that may be benefit you
https://paperswithcode.com/task/3d-object-detection
Thank you everyone,
I really appreciate your help and time. Unfortuantely, I cannot use ML or DL algorithms since I do not have enough data to train the network. Any other work around is required.
Thanks again!
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