That is something I don't really understand exactly why. 3D Models provide in one dataset the whole geometry information of each part, making it easier to learn and recognize that part in the real world, wouldn't it?
Is it because these ML algos lack a way to internally interpret 3D models for learning?
On top of that you reduce effort in data labeling, as each model would come with the relevant part ID, shape and color.
Of course, finetuning can be done afterwards with real world photos to increase robustness.
Is it because these ML algos lack a way to internally interpret 3D models for learning?
On top of that you reduce effort in data labeling, as each model would come with the relevant part ID, shape and color.
Of course, finetuning can be done afterwards with real world photos to increase robustness.