# A Conditional Adversarial Network for Scene Flow Estimation

## TL;DR

This paper introduces SceneFlowGAN, a novel conditional adversarial network that estimates scene flow, including optical flow and disparity, from stereo images efficiently, addressing real-world application challenges.

## Contribution

First to apply generative adversarial networks to scene flow estimation, enabling simultaneous optical flow and disparity prediction with reduced computational overhead.

## Key findings

- Effective on large RGB-D scene flow dataset
- Estimates both optical flow and disparity simultaneously
- Reduces computational overhead compared to traditional methods

## Abstract

The problem of Scene flow estimation in depth videos has been attracting attention of researchers of robot vision, due to its potential application in various areas of robotics. The conventional scene flow methods are difficult to use in reallife applications due to their long computational overhead. We propose a conditional adversarial network SceneFlowGAN for scene flow estimation. The proposed SceneFlowGAN uses loss function at two ends: both generator and descriptor ends. The proposed network is the first attempt to estimate scene flow using generative adversarial networks, and is able to estimate both the optical flow and disparity from the input stereo images simultaneously. The proposed method is experimented on a large RGB-D benchmark sceneflow dataset.