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We propose a novel 3D spatial representation for data fusion and scenereconstruction. Probabilistic Signed Distance Function (Probabilistic SDF,PSDF) is proposed to depict uncertainties in the 3D space. It is modeled by ajoint distribution describing SDF value and its inlier probability, reflectinginput data quality and surface geometry. A hybrid data structure involvingvoxel, surfel, and mesh is designed to fully exploit the advantages of variousprevalent 3D representations. Connected by PSDF, these components reasonablycooperate in a consistent frame- work. Given sequential depth measurements,PSDF can be incrementally refined with less ad hoc parametric Bayesianupdating. Supported by PSDF and the efficient 3D data representation,high-quality surfaces can be extracted on-the-fly, and in return contribute toreliable data fu- sion using the geometry information. Experiments demonstratethat our system reconstructs scenes with higher model quality and lowerredundancy, and runs faster than existing online mesh generation systems.

PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction