IRS

IRS: A Large Synthetic Indoor Robotics Stereo Dataset for Disparity and Surface Normal Estimation

Introduction

IRS is an open dataset for indoor robotics vision tasks, especially disparity and surface normal estimation. It contains totally 103,316 samples covering a wide range of indoor scenes, such as home, office, store and restaurant.

Left image	Right image
Disparity map	Surface normal map

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Overview of IRS

Rendering Characteristic	Options
indoor scene class	home(31145), office(43417), restaurant(22058), store(6696)
object class	desk, chair, sofa, glass, mirror, bed, bedside table, lamp, wardrobe, etc.
brightness	over-exposure(>1300), darkness(>1700)
light behavior	bloom(>1700), lens flare(>1700), glass transmission(>3600), mirror reflection(>3600)

We give some sample of different indoor scene characteristics as follows.

Home	Office	Restaurant
Normal light	Over exposure	Darkness
Glass	Mirror	Metal

Network Structure of DispNormNet

We design a novel network, namely DispNormNet, to estimate the disparity map and surface normal map together of the input stereo images. DispNormNet is comprised of two modules, DispNetC and NormNetDF. DispNetC is identical to that in this paper and produces the disparity map. NormNetDF produces the normal map and is similar to DispNetS. "DF" indicates disparity feature fusion, which we found important to produce accurate surface normal maps.

DispNormNet

Paper

Q. Wang^,1, S. Zheng,1, Q. Yan^*,2, F. Deng², K. Zhao^†,1, X. Chu^†,1.

IRS : A Large Synthetic Indoor Robotics Stereo Dataset for Disparity and Surface Normal Estimation. [preprint]

* indicates equal contribution. † indicates corresponding authors.
¹Department of Computer Science, Hong Kong Baptist University. ²School of Geodesy and Geomatics, Wuhan University.

Download

You can use the following BaiduYun link to download our dataset. More download links, including Google Drive and OneDrive, will be provided soon.

BaiduYun: https://pan.baidu.com/s/1VKVVdljNdhoyJ8JdQUCwKQ

Video Demonstration

Contact

Please contact us at qiangwang@comp.hkbu.edu.hk if you have any question.