# Video super-resolution for single-photon LIDAR

arXiv: https://arxiv.org/abs/2210.10474 · HTML: https://mlsanity.com/paper/2210.10474/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 44 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Understanding SSIM](https://mlsanity.com/paper/2006.13846/) (2020)
- [Real-World Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/2103.02368/) (2021)
- [Video Super Resolution Based on Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2007.12928/) (2020)
- [A Formal Evaluation of PSNR as Quality Measurement Parameter for Image Segmentation Algorithms](https://mlsanity.com/paper/1605.07116/) (2016)
- [Generalized Huber Loss for Robust Learning and its Efficient Minimization for a Robust Statistics](https://mlsanity.com/paper/2108.12627/) (2021)
- [BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation](https://mlsanity.com/paper/2107.12541/) (2021)
- [An Overview of Depth Cameras and Range Scanners Based on Time-of-Flight Technologies](https://mlsanity.com/paper/2012.06772/) (2020)
- [iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks](https://mlsanity.com/paper/2006.11161/) (2020)
- [Spatial images from temporal data](https://mlsanity.com/paper/1912.01413/) (2019)
- [Robust super-resolution depth imaging via a multi-feature fusion deep network](https://mlsanity.com/paper/2011.11444/) (2020)
- [High-speed object detection with a single-photon time-of-flight image sensor](https://mlsanity.com/paper/2107.13407/) (2021)
- [Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network](https://mlsanity.com/paper/1607.01977/) (2016)
- [A direct time-of-flight image sensor with in-pixel surface detection and dynamic vision](https://mlsanity.com/paper/2209.11772/) (2022)

## Built on by (most cited first, 3 of 3)

- [A Survey of Deep Learning Video Super-Resolution](https://mlsanity.com/paper/2506.03216/) (2025)
- [Point upsampling networks for single-photon sensing](https://mlsanity.com/paper/2508.12986/) (2025)
- [Hardware-aware Coding Function Design for Compressive Single-Photon 3D Cameras](https://mlsanity.com/paper/2510.12123/) (2025)

## Abstract (arXiv)

3D Time-of-Flight (ToF) image sensors are used widely in applications such as self-driving cars, Augmented Reality (AR) and robotics. When implemented with Single-Photon Avalanche Diodes (SPADs), compact, array format sensors can be made that offer accurate depth maps over long distances, without the need for mechanical scanning. However, array sizes tend to be small, leading to low lateral resolution, which combined with low Signal-to-Noise Ratio (SNR) levels under high ambient illumination, may lead to difficulties in scene interpretation. In this paper, we use synthetic depth sequences to train a 3D Convolutional Neural Network (CNN) for denoising and upscaling (x4) depth data. Experimental results, based on synthetic as well as real ToF data, are used to demonstrate the effectiveness of the scheme. With GPU acceleration, frames are processed at >30 frames per second, making the approach suitable for low-latency imaging, as required for obstacle avoidance.