Please use this identifier to cite or link to this item:
Title: RGBD indoor semantic segmentation with segmentation transformer
Authors: Choong, Han Yi
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Choong, H. Y. (2022). RGBD indoor semantic segmentation with segmentation transformer. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: SCSE21-0541
Abstract: 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.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
  Restricted Access
11.67 MBAdobe PDFView/Open

Page view(s)

Updated on May 19, 2022


Updated on May 19, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.