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Online semantic 3D segmentation in company with real-time RGB-Dreconstruction poses special challenges such as how to perform 3D convolutiondirectly over the progressively fused 3D geometric data, and how to smartlyfuse information from frame to frame. We propose a novel fusion-aware 3D pointconvolution which operates directly on the geometric surface beingreconstructed and exploits effectively the inter-frame correlation for highquality 3D feature learning. This is enabled by a dedicated dynamic datastructure which organizes the online acquired point cloud with global-localtrees. Globally, we compile the online reconstructed 3D points into anincrementally growing coordinate interval tree, enabling fast point insertionand neighborhood query. Locally, we maintain the neighborhood information foreach point using an octree whose construction benefits from the fast query ofthe global tree.Both levels of trees update dynamically and help the 3Dconvolution effectively exploits the temporal coherence for effectiveinformation fusion across RGB-D frames.

Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation