MaMirrorWindowDetection

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Glass/Mirror Detection

Abstract

Mirror and transparent-objects have been an issue for simultaneous re-localization and mapping (SLAM). Mirrors reflect light rays which cause wrong reconstructions and windows can not directly be measured by the depth camera. This is especially dangerous for robotic applications, since robots may try to go into a mirror or go through a window. The main goal of this work is to solve this issue by detecting mirrors/windows and reconstructing a correct map.

The potential approach is to use an object detection network, such as YOLO, to detect possible mirrors and windows. Then designing a function to correctly reconstruct the reflected region in the map.

This work involves knowledge in deep learning and SLAM.

Requirements

Python and C++
Deep learning in Object detection.
Computer vision and multiview geometry


robot_mirror

Literature

YOLOv3: An Incremental Improvement
MIRROR IDENTIFICATION AND CORRECTION OF 3D POINT CLOUDS
Dealing with Laser Scanner Failure: Mirrors and Windows
Detection and Segmentation of Mirror-like Surfaces Using Structured Illumination


Students.ProjectForm
Title: Glass/Mirror Detection
Abstract: Mirror and transparent-objects have been an issue for simultaneous re-localization and mapping (SLAM). Mirrors reflect light rays which cause the wrong reconstruction and windows are hard to be observed by cameras. This is especially dangerous for robotics since robots may try to go into a mirror or go through a window. The main goal of this work is to solve this issue by detecting mirrors/windows and reconstructing a correct map. The potential approach is to use an object detection network, such as YOLO, to detect possible mirrors and windows. Then designing a function to correctly reconstruction the reflected region in the map. This work involves knowledge in deep learning and SLAM.
Student:  
Director: Federico Tombari
Supervisor: Shun-Cheng Wu
Type: DA/MA/BA
Area:  
Status: draft
Start:  
Finish:  
Thesis (optional):  
Picture:  


Edit | Attach | Refresh | Diffs | More | Revision r1.5 - 23 Jan 2020 - 16:36 - ShunChengWu