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

Wei Dong, Qiuyuan Wang, Xin Wang, Hongbin Zha

ECCV 2018

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.

Citation

@inproceedings{dong2018psdf,
  title={PSDF fusion: Probabilistic signed distance function for on-the-fly 3D data fusion and scene reconstruction},
  author={Dong, Wei and Wang, Qiuyuan and Wang, Xin and Zha, Hongbin},
  booktitle={ECCV},
  pages={701--717},
  year={2018}
}