Depth estimation is one of the most important problems in computer vision. It has
attracted a lot of attention because it has applications in many areas, such as robotics,
VR and AR, self-driving cars etc. Using the defocus blur of a camera lens is one of
the methods of depth estimation. In this thesis, we have researched this technique in
virtual environments. Virtual datasets have been created for this purpose.
In this research, we have applied graph cuts and convolutional neural network
(DfD-net) to estimate depth from defocus blur using a natural (Middlebury) and a
virtual (Maya) dataset. Graph Cuts showed similar performance for both natural and
virtual datasets in terms of NMAE and NRMSE. However, with regard to SSIM, the
performance of graph cuts is 4% better for Middlebury compared to Maya.
We have trained the DfD-net using the natural and the virtual dataset and then
combining both datasets. The network trained by the virtual dataset performed best
for both datasets.
The performance of graph-cuts and DfD-net have been compared. Graph-Cuts
performance is 7% better than DfD-Net in terms of SSIM for Middlebury images. For
Maya images, DfD-Net outperforms Graph-Cuts by 2%. With regard to NRMSE,
Graph-Cuts and DfD-net shows similar performance for Maya images. For Middlebury
images, Graph-cuts is 1.8% better. The algorithms show no difference in performance
in terms of NMAE. The time DfD-net takes to generate depth maps compared
to graph cuts is 500 times less for Maya images and 200 times less for Middlebury
images.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7416254 |
| Date | 17 January 2019 |
| Creators | Prodipto Chowdhury (5931032) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/ESTIMATION_OF_DEPTH_FROM_DEFOCUS_BLUR_IN_VIRTUAL_ENVIRONMENTS_COMPARING_GRAPH_CUTS_AND_CONVOLUTIONAL_NEURAL_NETWORK/7416254 |
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