This repository contains implementation of Neuromorphic Fringe Projection Profilometry (NFPP) method proposed by Rao Mangalore et. al.
The data in the experiments folder have been collected from a DAVIS346 Camera. The drivers developed by the robotics and perception group were used to collect data from the DAVIS.
An object is placed on a platform and a moving light source generated in generator.py is made to fall on the object. The videos folder contains different patterns used with the light source generated using the generator file. Some examples of Patterns are shown below.
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The event camera, placed in a stereo configuration with the light projector ,records the Wrapped phase of the incident light. The phase is then unwrapped and triangulated to get depth maps.
Before starting the execution, please download the data from this link
Once you have downloaded files from the above link and extracted them to a folder called data/, run the files execute with the following commands:
python correlate_and_phase.py
The above command would compute wrapped phase at all the pixel locations. We then run the following command,
python shadow_fill_and_depth.py
This commmand compensates for shadows and calculates the final unwrapped phase and the depth maps. The wrapped and unwrapped phase calculated for various objects are shown below,
Note that the correlate_and_phase.py script has to be first run for the reference fringes i.e We must first calculate wrapped phase without any objects placed. We then move on to change the object name to calculate depth maps for different objects. To run for different object change the object_type and spatial_frequency variables in the main() function in the correlate_and_phase.py file. The same applies to the shadow_fill_and_depth.py file. In case you are using a camera other than DAVIS346, camera dimensions can be changed by changing the cam_dims variable. The output is a .xyz file. The .xyz files can be imported in meshlab and meshed to get 3D results.
The depth maps obtained are as follows
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The meshes look as shown below
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Below are the links to different types of Event Cameras,
If you happen to use this repository in your work or research please cite it as follows:
A. R. Mangalore, C. S. Seelamantula and C. S. Thakur, "Neuromorphic Fringe Projection Profilometry," in IEEE Signal Processing Letters, vol. 27, pp. 1510-1514, 2020, doi: 10.1109/LSP.2020.3016251.
@ARTICLE{mangalore2020nfpp,
author={A. R. {Mangalore} and C. S. {Seelamantula} and C. S. {Thakur}},
journal={IEEE Signal Processing Letters},
title={Neuromorphic Fringe Projection Profilometry},
year={2020},
volume={27},
pages={1510-1514},}