RGBD indoor semantic segmentation with segmentation transformer - Choong, Han Yi (Nanyang Technological University, 2022)
Depth information has proven to be a useful cue in the semantic segmentation of RGB-D images for providing a geometric counterpart to the RGB representation. Modern approaches typically formulate semantic segmentation as a per-pixel classification task, while instance-level segmentation uses an alternative mask classification. Recent insight is that mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks in a unified manner using the exact same model, loss, and training procedure. Following this observation, this project aims to study segmentation transformers under RGBD setting with a simple mask classification model which predicts a set of binary masks, each associated with a single global class label prediction.