How to return polygon bound or 3d image segmentation for a detected object with TensorFlow Lite and MLKit?

(posted here for more advice)

I am making a project that utilizes MLKit. The classification
model will be a TensorFlow Lite model. I noticed that the detected
objects always return rectangular bounding boxes. I would like them
to return polygonal bounds that are shaped like the object it is
detecting, or if possible, a sort of “3D” bound.

I am aware of certain annotation tools, along with things like
Mask RCNN, but I am not sure how to integrate them into a
TensorFlow Lite model, & I do not know what specific files to
edit. (or if I am supposed to implement it in the model rather than
the base code) or if I can even do it at all.

I want the detected objects to return bounding polygons, or even
3D polygons/image segmentations, instead of bounding boxes, using
MLKit + TensorFlow Lite. How do I achieve this?

submitted by /u/0zeroBudget

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