R-CNN Deep Learning with 3D Data
1 次查看(过去 30 天)
显示 更早的评论
Is it possible to "Train Object Detector Using R-CNN Deep Learning" with 3D data?
3D data: [x,y,z] and not [x,y,channel]
In this case, how do we define de "bounding boxes"?
For the 2D case is something like [x y width height]. And for a set of 2D images (i.e., 3D data)?
Any help please??
Thank you very much in advance!!
Best regards
0 个评论
采纳的回答
Shashank Gupta
2020-10-15
Hi Ana,
Generalising the 2d RCNN model to 3d is not so easy as it seems. the relevance that you intent to make from 2d and then trying to create a 3d architecture may not result in good performance, Although you can give a shot. So, there are few things you need to change. The input of 3d data should look something in the format [x,y,z,channel,batch_size] and the bounding boxes here will be cuboid, so the format will look somthing like [XMIN YMIN ZMIN WIDTH HEIGHT DEPTH]. You also need to change the layer to their respective 3d layers and write the custom training loop to train.
Hope this sounds good or atleast I provide you enough information to explore.
Cheers.
更多回答(0 个)
另请参阅
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!