PSDF Fusion: Probabilistic Signed Distance
Function for On-the-fly 3D Data Fusion and
Scene Reconstruction
Abstract. We propose a novel 3D spatial representation for data fusion
and scene reconstruction. Probabilistic Signed Distance Function (Probabilistic SDF, PSDF) is proposed to depict uncertainties in the 3D space.
It is modeled by a joint distribution describing SDF value and its inlier
probability, reflecting input data quality and surface geometry. A hybrid
data structure involving voxel, surfel, and mesh is designed to fully exploit the advantages of various prevalent 3D representations. Connected
by PSDF, these components reasonably cooperate in a consistent framework. Given sequential depth measurements, PSDF can be incrementally refined with less ad hoc parametric Bayesian updating. Supported
by PSDF and the efficient 3D data representation, high-quality surfaces
can be extracted on-the-fly, and in return contribute to reliable data fusion using the geometry information. Experiments demonstrate that our
system reconstructs scenes with higher model quality and lower redundancy, and runs faster than existing online mesh generation systems.