Dispersed Structured Light for Hyperspectral 3D Imaging

DSL (Dispersed Structured Light for Hyperspectral 3D Imaging) is a method that reconstructs high quality depth and hyperspectral information. You should follow the requirements.txt provided there.

Installation

git clone https://github.com/shshin1210/DSL.git
cd DSL
pip install -r requirements.txt

Datasets

You need to prepare three types of datasets for hyperspectral reconstruction. Refer to [DSL Supplementary](supplementary url) for more details.

1. Scene's depth map

   You should prepare depth reconstructed result using conventional structured light method under binary code patterns.

   Remember this is captured under a specific exposure time where first-order dispersion intensity is invalid.

2. Scene under white scan line pattern

   Capture the scene under white scan line pattern with two different intensity pattern values.

   Save it in path_to_ldr_exp1, path_to_ldr_exp2.

3. Scene under black pattern and white pattern

   We need scene captured under black pattern with two different exposure settings.

   Save it in path_to_black_exp1, path_to_black_exp2.

   Also, capture the scene under white pattern under two different intensity pattern values to calculate the radiance weight (normalization) for two different settings.

   Save it in path_to_intensity1, path_to_intensity2.

dataset
|-- depth.npy
|-- intensity1
|-- intensity2
|-- black_exposure1
|-- black_exposure2
|-- ldr_exposure1
    |-- scene under white scanline pattern 0.png
    |-- scene under white scanline pattern 1.png
    |-- ...
|-- ldr_exposure2
    |-- scene under white scanline pattern 0.png
    |-- scene under white scanline pattern 1.png
    |-- ...

How To Run?

To reconstruct hyperspectral reflectance:

python hyper_sl/hyperspectral_reconstruction.py

replace any configuration changes in [ArgParse.py] file (https://github.com/shshin1210/DSL/blob/main/hyper_sl/utils/ArgParser.py).