  Incorporating the physical environment is essential for a complete
understanding of human behavior in unconstrained every-day tasks. This is
especially important in ego-centric tasks where obtaining 3 dimensional
information is both limiting and challenging with the current 2D video analysis
methods proving insufficient. Here we demonstrate a proof-of-concept system
which provides real-time 3D mapping and semantic labeling of the local
environment from an ego-centric RGB-D video-stream with 3D gaze point
estimation from head mounted eye tracking glasses. We augment existing work in
Semantic Simultaneous Localization And Mapping (Semantic SLAM) with collected
gaze vectors. Our system can then find and track objects both inside and
outside the user field-of-view in 3D from multiple perspectives with reasonable
accuracy. We validate our concept by producing a semantic map from images of
the NYUv2 dataset while simultaneously estimating gaze position and gaze
classes from recorded gaze data of the dataset images.
