# SinglePhotonLidar
### *class* deepinv.physics.SinglePhotonLidar(sigma=1.0, bins=50, device='cpu', rng=None)
Bases: [`Physics`](https://deepinv.org/api/stubs/deepinv.physics.Physics.html.md#deepinv.physics.Physics)
Single photon lidar operator for depth ranging.
See Rapp *et al.*[1](#footcite-rapp2020advances) for a review of this imaging method.
The forward operator is given by
$$
y_{i,j,t} = \mathcal{P}(h(t-d_{i,j}) r_{i,j} + b_{i,j})
$$
where $\mathcal{P}$ is the Poisson noise model, $h(t)$ is a Gaussian impulse response function at
time $t$, $d_{i,j}$ is the depth of the scene at pixel $(i,j)$,
$r_{i,j}$ is the intensity of the scene at pixel $(i,j)$ and $b_{i,j}$ is the background noise
at pixel $(i,j)$.
For a pixel grid of size (H,W) and batch size B, the signals have size (B, 3, H, W), where the first channel
contains the depth of the scene $d$, the second channel contains the intensity of the scene $r$ and
the third channel contains the per pixel background noise levels $b$.
* **Parameters:**
* **sigma** ([*float*](https://docs.python.org/3.12/library/functions.html#float)) – Standard deviation of the Gaussian impulse response function.
* **bins** ([*int*](https://docs.python.org/3.12/library/functions.html#int)) – Number of histogram bins per pixel.
* **device** ([*str*](https://docs.python.org/3.12/library/stdtypes.html#str)) – Device to use (gpu or cpu).
* **rng** ([*torch.Generator*](https://docs.pytorch.org/docs/stable/generated/torch.Generator.html#torch.Generator)) – (optional) a pseudorandom random number generator for
the Poisson noise model [`deepinv.physics.PoissonNoise`](https://deepinv.org/api/stubs/deepinv.physics.PoissonNoise.html.md#deepinv.physics.PoissonNoise)
* **References:**
* **[1]** Joshua Rapp, Julián Tachella, Yoann Altmann, Stephen McLaughlin, and Vivek K Goyal. Advances in single-photon lidar for autonomous vehicles: working principles, challenges, and recent advances. *IEEE Signal Processing Magazine*, 37(4):62–71, 2020.
#### A(x, \*\*kwargs)
Applies the forward operator.
Input is of size (B, 3, H, W) and output is of size (B, bins, H, W)
* **Parameters:**
**x** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – tensor containing the depth, intensity and background noise levels.
#### A_dagger(y, \*\*kwargs)
Applies Matched filtering to find the peaks.
Input is of size (B, bins, H, W), output of size (B, 3, H, W).
* **Parameters:**
**y** ([*torch.Tensor*](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) – measurements
## Examples using `SinglePhotonLidar`:
[Single photon lidar operator for depth ranging.](https://deepinv.org/auto_examples/physics/demo_lidar.html.md)