THREE DIMENSIONAL RECONSTRUCTION FROM SINGLE VIEW TWO DIMENSIONAL IMAGE
Methods for the reconstruction of three-dimensional models from a single view twodimensional image have developed rapidly in recent years. However, most of these methods are still in the form of research with a very controlled form of input. For example, in the Pixelaligned Implicit Function meth...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/48221 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Methods for the reconstruction of three-dimensional models from a single view twodimensional
image have developed rapidly in recent years. However, most of these methods
are still in the form of research with a very controlled form of input. For example, in the Pixelaligned
Implicit Function method, reconstruction has succeeded in achieving a threedimensional
model with highly detailed human shapes and textures from a single view twodimensional
image, but the input image is limited by various criteria and must include a mask
image that shows the human position in the image. Therefore, this final project aims to build
applications that can reconstruct a three-dimensional model of a single view two-dimensional
image with broader criteria and do not require additional input other than the relevant twodimensional
picture.
For this reason, a segmentation module will be created that uses a semantic
segmentation method named Fully Convolutonal DenseNet to produce the mask image needed
for reconstruction. This segmentation module will also allow for handling object reconstruction
for images containing more than one object.
The use of segmentation modules results in a decrease in the quality of reconstruction
but it also increases automation and enlarges the range of inputs that can be processed. |
|---|