RGBD2lux: Dense light intensity estimation with an RGBD sensor

TL;DR

This paper introduces an automated computer vision system that estimates dense light intensity in indoor environments using a single RGBD sensor, improving upon traditional manual and simulation-based methods.

Contribution

It presents the first automatic framework for indoor lighting estimation from RGBD data, combining depth and RGB images for dense light measurement.

Findings

Outperforms existing commercial light-planning software

Provides dense light intensity maps from minimal sensor input

Automates the lighting measurement process

Abstract

Lighting design and modelling or industrial applications like luminaire planning and commissioning rely heavily on time consuming manual measurements or on physically coherent computational simulations. Regarding the latter,standard approaches are based on CAD modeling simulations and offline rendering, with long processing times and therefore inflexible workflows. Thus, in this paper we pro-pose a computer vision based system to measure lighting with just a single RGBD camera. The proposed method uses both depth data and images from the sensor to provide a dense measure of light intensity in the field of view of the camera. We evaluate our system on novel ground truth data and compare it to state-of-the-art commercial light-planning software. Our system provides improved performance, while being completely automated, given that the CAD model is extracted from the depth and the albedo estimated with the support of RGB images. To the best of our knowledge, this is the first automatic framework for the estimation of lighting in general indoor scenarios from RGBDinput.