Install InteriorNet resouce.

README.md

@@ -150,6 +150,11 @@ To see a survey of RGBD datasets, check out Michael Firman's [collection](http:/
 <br>This platform provides RGB from 1000 point clouds, as well as multimodal sensor data: surface normal, depth, and for a fraction of the spaces, semantics object annotations. The environment is also RL ready with physics integrated. Using such datasets can further narrow down the discrepency between virtual environment and real world.
 <p align="center"><img width="50%" src="https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Gibson%20Environment-%20Real-World%20Perception%20for%20Embodied%20Agents%20(2018%20CVPR)%20.jpeg" /></p>
 
+<b>InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset</b> [[Link]](https://interiornet.org/)
+<br>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.
+<p align="center"><img width="50%" src="https://interiornet.org/items/InteriorNet.jpg" /></p>
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+
 <a name="pose_estimation" />
 
 ## 3D Pose Estimation