PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction
This work addresses the challenge of efficient and accurate real-time 3D reconstruction for applications like robotics or augmented reality, representing an incremental improvement with a novel hybrid approach.
The paper tackles the problem of 3D scene reconstruction from sequential depth data by proposing a Probabilistic Signed Distance Function (PSDF) to model uncertainties and a hybrid data structure, resulting in higher model quality, lower redundancy, and faster runtime compared to existing online mesh generation systems.
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 frame- work. 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 fu- sion 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.