We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D
SLAM system that goes beyond traditional systems which output a purely
geometric map of a static scene. MaskFusion recognizes, segments and assigns
semantic class labels to different objects in the scene, while tracking and
reconstructing them even when they move independently from the camera.
As an RGB-D camera scans a cluttered scene, image-based instance-level
semantic segmentation creates semantic object masks that enable real-time
object recognition and the creation of an object-level representation for the
world map. Unlike previous recognition-based SLAM systems, MaskFusion does not
require known models of the objects it can recognize, and can deal with
multiple independent motions. MaskFusion takes full advantage of using
instance-level semantic segmentation to enable semantic labels to be fused into
an object-aware map, unlike recent semantics enabled SLAM systems that perform
voxel-level semantic segmentation. We show augmented-reality applications that
demonstrate the unique features of the map output by MaskFusion:
instance-aware, semantic and dynamic.

MaskFusion 是一种实时、对象感知、语义和动态的 RGB-D SLAM 系统，利用图像实例级语义分割创建语义对象掩码，对物体进行识别和重建，不需要已知的物体模型，发展到实现实例感知、语义和动态等增强现实应用。