  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
http://robotvault.bitbucket.io/scenenet-rgbd.html
