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

SceneNet  RGB-D 提供了室内场景轨迹的大规模真实渲染，为场景理解和几何计算机视觉问题提供像素级完美的标签数据，以及适用于从头开始使用 RGB-D 输入的数据驱动的计算机视觉技术的预训练数据集，并且也提供了探索 3D 场景标注任务的基础。