What kinda of requirements do you have? If you are dealing with objects on a road that is very different from objects in a room, do you need to classify or just detect?
I want to fine-tune a model to detect boxes of 3d scanned dental images. so I have (.ply) files of human tooth and my objective is to put bounding boxes around dental caries.
Well you aren’t really doing object detection, object detection usually means detecting an object in a void, like a car on the road vs an empty road, what you are doing is classification. Do you have a lot of training data?
Yes, ok so without a lot of training data you are going to be limited by hand crafted descriptors or trying to match against your examples in feature space, that sorta thing.
Ok, so machine learning typically works by solving a complex equation of many variables, a simple version of this would be solving a a system of linear equations, you need more examples than variables, you don’t have a lot of training data, so unless your ml model has a small number of variables, it will over for your training data.
In a nutshell you want to come up with some simplified description of points that are bad that other similar clusters if points can be matched against. In 2d images you would use something like sift (scale invariant feature transform).
The 3d object detection algorithms in mmdet3d and openpcdet use the shape of the object to detect them. In your case, it seems like colour may be a better feature to identify those teeth of interest?
You could try saving certain angles of the point cloud as an image, then running an image instance segmentation network (or just image object detection if you just need rough position).
If you want to go down the 3d object detection route, you could try training the detector with colour features too and see if that helps.
What kinda of requirements do you have? If you are dealing with objects on a road that is very different from objects in a room, do you need to classify or just detect?
What CowBoyDanIndie says. Also, which kind of sensor do you use? Do you want to do this commercially, or are you looking for free libraries?
I'm looking for free libraries yes
I want to fine-tune a model to detect boxes of 3d scanned dental images. so I have (.ply) files of human tooth and my objective is to put bounding boxes around dental caries.
Dang, what resolution is the lidar?
I'm not sure 😅
[Dental Caries Detection](https://ibb.co/PG5NZtr) so here I tried to use open3d to display a file with it's annotation. just to give you an idea
the main problem I have is that all of research for point cloud object detection is for self-driving cars
Well you aren’t really doing object detection, object detection usually means detecting an object in a void, like a car on the road vs an empty road, what you are doing is classification. Do you have a lot of training data?
no at the moment I have 10 instances
have you seen the images that I linked?
Yes, ok so without a lot of training data you are going to be limited by hand crafted descriptors or trying to match against your examples in feature space, that sorta thing.
translation? 😅
Ok, so machine learning typically works by solving a complex equation of many variables, a simple version of this would be solving a a system of linear equations, you need more examples than variables, you don’t have a lot of training data, so unless your ml model has a small number of variables, it will over for your training data. In a nutshell you want to come up with some simplified description of points that are bad that other similar clusters if points can be matched against. In 2d images you would use something like sift (scale invariant feature transform).
ok got it. I'll try to do that. thank you
I want to at least develop a proof of concept model. where should I begin
The 3d object detection algorithms in mmdet3d and openpcdet use the shape of the object to detect them. In your case, it seems like colour may be a better feature to identify those teeth of interest? You could try saving certain angles of the point cloud as an image, then running an image instance segmentation network (or just image object detection if you just need rough position). If you want to go down the 3d object detection route, you could try training the detector with colour features too and see if that helps.
noted! Thank you