Paper Info
Title
SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth.
Abstract
We introduce SceneNet RGB-D, expanding the previous work of SceneNet to enable large scale photorealistic rendering of indoor scene trajectories. It provides pixel-perfect ground truth for scene understanding problems such as semantic segmentation, instance segmentation, and object detection, and also for geometric computer vision problems such as optical flow, depth estimation, camera pose estimation, and 3D reconstruction. Random sampling permits virtually unlimited scene configurations, and here we provide a set of 5M rendered RGB-D images from over 15K trajectories in synthetic layouts with random but physically simulated object poses. Each layout also has random lighting, camera trajectories, and textures. The scale of this dataset is well suited for pre-training data-driven computer vision techniques from scratch with RGB-D inputs, which previously has been limited by relatively small labelled datasets in NYUv2 and SUN RGB-D. It also provides a basis for investigating 3D scene labelling tasks by providing perfect camera poses and depth data as proxy for a SLAM system. We host the dataset at this http URL
Year
Venue
Field
2016
arXiv: Computer Vision and Pattern Recognition
Object detection,Computer vision,Computer graphics (images),Computer science,Segmentation,Pose,Ground truth,RGB color model,Artificial intelligence,Rendering (computer graphics),Optical flow,3D reconstruction
DocType
Volume
Citations 
Journal
abs/1612.05079
5
PageRank 
References 
Authors
0.39
0
4
Name
Order
Citations
PageRank
John McCormac1752.46
Ankur Handa247926.11
Stefan Leutenegger3137961.81
Andrew J. Davison46707350.85