  Online semantic 3D segmentation in company with real-time RGB-D
reconstruction poses special challenges such as how to perform 3D convolution
directly over the progressively fused 3D geometric data, and how to smartly
fuse information from frame to frame. We propose a novel fusion-aware 3D point
convolution which operates directly on the geometric surface being
reconstructed and exploits effectively the inter-frame correlation for high
quality 3D feature learning. This is enabled by a dedicated dynamic data
structure which organizes the online acquired point cloud with global-local
trees. Globally, we compile the online reconstructed 3D points into an
incrementally growing coordinate interval tree, enabling fast point insertion
and neighborhood query. Locally, we maintain the neighborhood information for
each point using an octree whose construction benefits from the fast query of
the global tree.Both levels of trees update dynamically and help the 3D
convolution effectively exploits the temporal coherence for effective
information fusion across RGB-D frames.
