Towards Probabilistic Volumetric Reconstruction using Ray Potentials

2015

Conference Paper

avg

ps


This paper presents a novel probabilistic foundation for volumetric 3-d reconstruction. We formulate the problem as inference in a Markov random field, which accurately captures the dependencies between the occupancy and appearance of each voxel, given all input images. Our main contribution is an approximate highly parallelized discrete-continuous inference algorithm to compute the marginal distributions of each voxel's occupancy and appearance. In contrast to the MAP solution, marginals encode the underlying uncertainty and ambiguity in the reconstruction. Moreover, the proposed algorithm allows for a Bayes optimal prediction with respect to a natural reconstruction loss. We compare our method to two state-of-the-art volumetric reconstruction algorithms on three challenging aerial datasets with LIDAR ground truth. Our experiments demonstrate that the proposed algorithm compares favorably in terms of reconstruction accuracy and the ability to expose reconstruction uncertainty.

Award: (Best Paper Award)
Author(s): Ulusoy, Ali Osman and Geiger, Andreas and Black, Michael J.
Book Title: 3D Vision (3DV), 2015 3rd International Conference on
Pages: 10-18
Year: 2015
Month: October

Department(s): Autonomous Vision, Perceiving Systems
Research Project(s): Volumetric Reconstruction
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Address: Lyon
Award Paper: Best Paper Award
DOI: 10.1109/3DV.2015.9

Links: code
YouTube
Video:
Attachments: pdf
suppmat

BibTex

@inproceedings{3dv2015,
  title = {Towards Probabilistic Volumetric Reconstruction using Ray Potentials},
  author = {Ulusoy, Ali Osman and Geiger, Andreas and Black, Michael J.},
  booktitle = {3D Vision (3DV), 2015 3rd International Conference on},
  pages = {10-18},
  address = {Lyon},
  month = oct,
  year = {2015},
  month_numeric = {10}
}