1 / 3

SEM 3D Surface Reconstruction

Ahmad Pahlavan Tafti a , Zeyun Yu a , Andrew Kirkpatrick b , Heather A. Owen b a Department of Computer Science, University of Wisconsin Milwaukee b Department of Biological Sciences, University of Wisconsin Milwaukee . SEM 3D Surface Reconstruction. Problem Statement

lluvia
Download Presentation

SEM 3D Surface Reconstruction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Ahmad Pahlavan Tafti a, Zeyun Yu a, Andrew Kirkpatrick b, Heather A. Owen b aDepartment of Computer Science, University of Wisconsin Milwaukee b Department of Biological Sciences, University of Wisconsin Milwaukee SEM 3D Surface Reconstruction Problem Statement Restoring back the 3D surface model of a microscopic object is extremely difficult to solve while its three dimensional shape in a real world is only projected into 2D digital images using a Scanning Electron Microscope (SEM). Computer vision exposes a great ability to restore the geometry of the scene by solving the inversion problem going from 2D to 3D. Objectives To bring 3D technology for microscopic objects. To create realistic anatomic shape from microscopic objects. To allow rotation and depth for further interpretation of microscopic objects.

  2. We normally initialize the 3D points and relative poses with some error thresholds. The latter process is a minimization routine that optimizes the 3D geometry information and rotation/translation parameters together. Algorithm and Approach • We took Differential Evolution into account to solve this optimization problem.

  3. Input Output 3D Visualization Going from 2D to 3D We generated a 3D model of a Tapetal cell using only its 2D images. We solved inverse problem going from 2D to 3D. We took multiple 2D images from a Tapetal cell using Scanning Electron Microscope (SEM). Going from 2D to 3D We generated a 3D model of a Diatom using only its 2D images. We solved inverse problem going from 2D to 3D. We took multiple 2D images from a Diatom using Scanning Electron Microscope (SEM).

More Related