SceneNet

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Introduced by Handa et al. in SceneNet: Understanding Real World Indoor Scenes With Synthetic Data

SceneNet is a dataset of labelled synthetic indoor scenes. There are several labeled indoor scenes, including:

11 Bedroom scenes with 428 objects 15 Office scenes with 1,203 objects 11 Kitchen scenes with 797 objects 10 Living Room scenes with 715 objects 10 Bathrooms with 556 objects

Repository of Labelled Synthetic Indoor Scenes. This dataset is increasingly being used beyond standard computer vision problems e.g. semantic segmentation, optic flow, 3D scene reconstruction etc. to now physical scene understanding and Deep Reinforcement Learning with agents interacting with their 3D environments.

Publications

SceneNet: Understanding Real World Indoor Scenes With Synthetic Data, A. Handa, V. Patraucean, V. Badrinarayanan, S. Stent and R. Cipolla

Understanding Real World Indoor Scenes with Synthetic Data, A. Handa, V. Patraucean, V. Badrinarayanan, S. Stent and R. Cipolla, CVPR 2016

SceneNet: An Annotated Model Generator for Indoor Scene Understanding, A. Handa, V. Patraucean, S. Stent and R. Cipolla, ICRA 2016

License : https://creativecommons.org/licenses/by-nc/4.0/legalcode