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InteriorNet

Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset

InteriorNet

System Overview: an end-to-end pipeline to render an RGB-D-inertial benchmark for large scale interior scene understanding and mapping. Our dataset contains 20M images created by pipeline: (A) We collect around 1 million CAD models provided by world-leading furniture manufacturers. These models have been used in the real-world production. (B) Based on those models, around 1,100 professional designers create around 22 million interior layouts. Most of such layouts have been used in real-world decorations. (C) For each layout, we generate a number of configurations to represent different random lightings and simulation of scene change over time in daily life. (D) We provide an interactive simulator (ViSim) to help for creating ground truth IMU, events, as well as monocular or stereo camera trajectories including hand-drawn, random walking and neural network based realistic trajectory. (E) All supported image sequences and ground truth.

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Imperial College London
https://www.imperial.ac.uk
Task
3D Reconstruction / Photogrammetry
Annotation Types
3D Point Cloud
20000000
Items
1100
Classes
20000000
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
Unknown
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