# Defogging Kinect: Simultaneous Estimation of Object Region and Depth in Foggy Scenes

## TL;DR

This paper presents a novel method using a ToF camera to simultaneously estimate object regions and depth in foggy scenes, overcoming challenges posed by participating media for 3D reconstruction.

## Contribution

The method uniquely leverages scattering saturation and light attenuation phenomena to estimate scattering and depth simultaneously in foggy environments.

## Key findings

- Effective depth and object region estimation in fog using Kinect v2
- Robust IRLS optimization improves estimation accuracy
- Validated with real fog scenes and synthesized data

## Abstract

Three-dimensional (3D) reconstruction and scene depth estimation from 2-dimensional (2D) images are major tasks in computer vision. However, using conventional 3D reconstruction techniques gets challenging in participating media such as murky water, fog, or smoke. We have developed a method that uses a time-of-flight (ToF) camera to estimate an object region and depth in participating media simultaneously. The scattering component is saturated, so it does not depend on the scene depth, and received signals bouncing off distant points are negligible due to light attenuation in the participating media, so the observation of such a point contains only a scattering component. These phenomena enable us to estimate the scattering component in an object region from a background that only contains the scattering component. The problem is formulated as robust estimation where the object region is regarded as outliers, and it enables the simultaneous estimation of an object region and depth on the basis of an iteratively reweighted least squares (IRLS) optimization scheme. We demonstrate the effectiveness of the proposed method using captured images from a Kinect v2 in real foggy scenes and evaluate the applicability with synthesized data.