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
lib/lib_convnlos/mex/complie_mex.mto complie mex codes. - Run
demo.mscript. It includes Light Cone Transform (LCT), filtered backprojection, our method without priors, and our method with priors for comparison. - 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 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. |