# Weakly-Supervised Optical Flow Estimation for Time-of-Flight

arXiv: https://arxiv.org/abs/2210.05298 · HTML: https://mlsanity.com/paper/2210.05298/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [What Matters in Unsupervised Optical Flow](https://mlsanity.com/paper/2006.04902/) (2020)
- [Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness](https://mlsanity.com/paper/1608.05842/) (2016)
- [FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation](https://mlsanity.com/paper/2103.04524/) (2021)
- [Learning optical flow from still images](https://mlsanity.com/paper/2104.03965/) (2021)
- [Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset](https://mlsanity.com/paper/1807.10376/) (2018)
- [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)
- [RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising](https://mlsanity.com/paper/2111.15513/) (2021)

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

- [Robust Depth Enhancement via Polarization Prompt Fusion Tuning](https://mlsanity.com/paper/2404.04318/) (2024)

## Abstract (arXiv)

Indirect Time-of-Flight (iToF) cameras are a widespread type of 3D sensor, which perform multiple captures to obtain depth values of the captured scene. While recent approaches to correct iToF depths achieve high performance when removing multi-path-interference and sensor noise, little research has been done to tackle motion artifacts. In this work we propose a training algorithm, which allows to supervise Optical Flow (OF) networks directly on the reconstructed depth, without the need of having ground truth flows. We demonstrate that this approach enables the training of OF networks to align raw iToF measurements and compensate motion artifacts in the iToF depth images. The approach is evaluated for both single- and multi-frequency sensors as well as multi-tap sensors, and is able to outperform other motion compensation techniques.