tum-vision / sublabel_relax

Code for sublabel-accurate multi-labeling papers (published at CVPR '16, ECCV '16)

Sublabel-Accurate Convex Relaxations

Installation

To run the code, first install the convex optimization framework prost following the instructions presented there.

It is required to add some additional proximal and linear operators to prost. To do so, create the file CustomSources.cmake in the directory prost/cmake/ with the following contents:

set(PROST_CUSTOM_SOURCES
  "relative_path_to_sublabel_relax"/cvpr2016/prost/block_dataterm_sublabel.cu
  "relative_path_to_sublabel_relax"/cvpr2016/prost/prox_ind_epi_polyhedral_1d.cu
  "relative_path_to_sublabel_relax"/cvpr2016/prost/prox_ind_epi_conjquad_1d.cu
  "relative_path_to_sublabel_relax"/eccv2016/prost/prox_ind_epi_polyhedral.cu
  
  "relative_path_to_sublabel_relax"/cvpr2016/prost/block_dataterm_sublabel.hpp
  "relative_path_to_sublabel_relax"/cvpr2016/prost/prox_ind_epi_polyhedral_1d.hpp
  "relative_path_to_sublabel_relax"/cvpr2016/prost/prox_ind_epi_conjquad_1d.hpp
  "relative_path_to_sublabel_relax"/eccv2016/prost/prox_ind_epi_polyhedral.hpp
  )
  
set(MATLAB_CUSTOM_SOURCES
  "relative_path_to_sublabel_relax"/cvpr2016/prost/custom.cpp
  "relative_path_to_sublabel_relax"/eccv2016/prost/custom.cpp
  )

Replace "relative_path_to_sublabel_relax" with the relative path to go from the directory prost/cmake to the directory where you cloned this repository into (e.g., ../../sublabel_relax).

After adding this file, recompile prost again, e.g., run in the directory prost/build

cmake ..
make -j16

One-dimensional ranges (CVPR16)

Further instructions for reproducing the individual numerical experiments from the paper Sublabel-Accurate Relaxation of Nonconvex Energies can be found here.

Multi-dimensional ranges (ECCV16)

Further instructions for reproducing the individual numerical experiments from the paper Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies can be found here.

Publications

• Sublabel-Accurate Relaxation of Nonconvex Energies (T. Möllenhoff, E. Laude, M. Moeller, J. Lellmann, D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

• Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies (E. Laude, T. Möllenhoff, M. Moeller, J. Lellmann, D. Cremers), In European Conference on Computer Vision and Pattern Recognition (ECCV), 2016.