Transformers vs. Convolutional Networks for 3D segmentation in industrial CT data

Type: BA thesis

Status: running

Supervisors: Martin Leipert

The current state of the art for segmentation in industrial CT are oftentimes CNNs.
Transformer based models are sparsely used.
Therefore, this project wants to compare the semantic segmentation performance of transformers (that include global context into segmentation), pure convolutional neural networks (that use local context) and combined methods (like this one: on an industrial CT dataset of shoes like in this study: .

Only available as Bachelors thesis / Research Project