  Recent advances of 3D acquisition devices have enabled large-scale
acquisition of 3D scene data. Such data, if completely and well annotated, can
serve as useful ingredients for a wide spectrum of computer vision and graphics
works such as data-driven modeling and scene understanding, object detection
and recognition. However, annotating a vast amount of 3D scene data remains
challenging due to the lack of an effective tool and/or the complexity of 3D
scenes (e.g. clutter, varying illumination conditions). This paper aims to
build a robust annotation tool that effectively and conveniently enables the
segmentation and annotation of massive 3D data. Our tool works by coupling 2D
and 3D information via an interactive framework, through which users can
provide high-level semantic annotation for objects. We have experimented our
tool and found that a typical indoor scene could be well segmented and
annotated in less than 30 minutes by using the tool, as opposed to a few hours
if done manually. Along with the tool, we created a dataset of over a hundred
3D scenes associated with complete annotations using our tool. The tool and
dataset are available at www.scenenn.net.
