# Lightweight Deep Learning Architecture for MPI Correction and Transient Reconstruction

arXiv: https://arxiv.org/abs/2111.14396 · HTML: https://mlsanity.com/paper/2111.14396/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset](https://mlsanity.com/paper/1807.10376/) (2018)
- [An Overview of Depth Cameras and Range Scanners Based on Time-of-Flight Technologies](https://mlsanity.com/paper/2012.06772/) (2020)
- [DeepToF: Off-the-Shelf Real-Time Correction of Multipath Interference in Time-of-Flight Imaging](https://mlsanity.com/paper/1805.09305/) (2018)
- [iToF2dToF: A Robust and Flexible Representation for Data-Driven Time-of-Flight Imaging](https://mlsanity.com/paper/2103.07087/) (2021)
- [SRA: Fast Removal of General Multipath for ToF Sensors](https://mlsanity.com/paper/1403.5919/) (2014)

## Built on by (most cited first, 1 of 1)

- [NIGHT -- Non-Line-of-Sight Imaging from Indirect Time of Flight Data](https://mlsanity.com/paper/2403.19376/) (2024)

## Abstract (arXiv)

Indirect Time-of-Flight cameras (iToF) are low-cost devices that provide depth images at an interactive frame rate. However, they are affected by different error sources, with the spotlight taken by Multi-Path Interference (MPI), a key challenge for this technology. Common data-driven approaches tend to focus on a direct estimation of the output depth values, ignoring the underlying transient propagation of the light in the scene. In this work instead, we propose a very compact architecture, leveraging on the direct-global subdivision of transient information for the removal of MPI and for the reconstruction of the transient information itself. The proposed model reaches state-of-the-art MPI correction performances both on synthetic and real data and proves to be very competitive also at extreme levels of noise; at the same time, it also makes a step towards reconstructing transient information from multi-frequency iToF data.