3DMBDP / 3D_Drone_Reconstruction

2-D to 3-D Image Reconstruction of a telecommunication tower
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3d-reconstruction brisk deep-learning opencv sfm sift uav unet-image-segmentation

3D_Drone_Reconstruction

AIM: 2D to 3D reconstruction of telecommunication tower.

VisualSFM

A completely integration of 3D reconstruction software:

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SFM

We implemented SFM using MATLAB. For feature detection SURF was used.

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Feature Detectors

Get feature points, we used Lowe's program but failed to generate feasible solution with complex background and blur. Trying alternatives now:

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Unet

We implemented a Unet model to segment out tower from images with complex background.

Image Augmentation

130 masks were manually created and augmented. Images were rotated flipped and blurred to increase dataset size.

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Unet model

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Total images: 4000

Total masks (labels): 4000

Training loss: 0.2660

Training accuracy: 88.44%

Validation loss: 0.2744

Validation accuracy: 87.84%

Test accuracy: 87.93%

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