lishy66 / conv_nlos

Convolutional Approximations to the General Non-Line-of-Sight Imaging Operator

conv_nlos

This repsitory is an implementation of the method described in the following paper:

"Convolutional Approximations to the General Non-Line-of-Sight Imaging Operator"
Byeongjoo Ahn, Akshat Dave, Ashok Veeraraghavan, Ioannis Gkioulekas, and Aswin C. Sankaranarayanan
IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Oral Presentation)

Run Code

1. Run lib/lib_convnlos/mex/complie_mex.m to complie mex codes.
2. Run demo.m script. It includes Light Cone Transform (LCT), filtered backprojection, our method without priors, and our method with priors for comparison.
3. Results are stored in results/[obj_name]/. It includes runtime for each algorithm, and reconstructed NLOS albedo and its 2D maximum intensity projection in three different colormaps.

Inputs

Inputs are stored in data/[obj_name]_transient.mat . It includes NLOSDATA structure consisting of the following parameters.

Parameter	Description
obj_name	Object name.
transient	Light transient [n_l * n_s * n_t]. Transients need to be rectified such that the direct component starts at t = 0.
l	Illumination points [n_l * 3].
s	Sensing points [n_s *3].
times	Time bins [1* n_t].
is_confocal	Scanning pattern (1 for confocal, 0 for 5D).
target_dist	Distance from LOS wall to object center (m). Object is located at origin, and LOS wall is at z = target_dist.
delta	ToF resolution (m).
n_voxel	Voxel resolution for NLOS albedo [n_x * n_y * n_z].
x / y / z	Voxel position for NLOS albedo [1 * n_x] / [1 * n_y] / [1 * n_z].
n_voxel_kernel	Voxel resolution for kernel.
x_kernel / y_kernel / z_kernel	Voxel position for kernel.
n_sample_per_dim	Kernel sampling resolution. We use n_sample_per_dim = 3, which means the kernels are sampled from 3 * 3 * 3 = 27 points to make a kernel basis.
x_sample / y_sample / z_sample	Kernel sampling location.