Feature-Preserving Mesh Decimation for Normal Integration

Normal integration reconstructs 3D surfaces from normal maps obtained e.g. by photometric stereo. These normal maps capture surface details down to the pixel level but require large computational resources for integration at high resolutions. In this work, we replace the dense pixel grid with a sparse anisotropic triangle mesh prior to normal integration. We adapt the triangle mesh to the local geometry in the case of complex surface structures and remove oversampling from flat featureless regions. For high-resolution images, the resulting compression reduces normal integration runtimes from hours to minutes while maintaining high surface accuracy. Our main contribution is the derivation of the well-known quadric error measure from mesh decimation for screen space applications and its combination with optimal Delaunay triangulation. Code is available at https://moritzheep.github.io/anisotropic-screen-meshing.

  • Published in:
    {IEEE/CVF} Conference on Computer Vision and Pattern Recognition, {CVPR} 2025, Nashville, TN, USA, June 11-15, 2025
  • Type:
    Inproceedings
  • Authors:
    Heep, Moritz; Behnke, Sven; Zell, Eduard
  • Year:
    2025
  • Source:
    https://ieeexplore.ieee.org/document/11093194

Citation information

Heep, Moritz; Behnke, Sven; Zell, Eduard: Feature-Preserving Mesh Decimation for Normal Integration, {IEEE/CVF} Conference on Computer Vision and Pattern Recognition, {CVPR} 2025, Nashville, TN, USA, June 11-15, 2025, 2025, 5783--5792, June, https://ieeexplore.ieee.org/document/11093194, Heep.etal.2025a,

Associated Lamarr Researchers

lamarr institute person Behnke Sven - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Sven Behnke

Area Chair Embodied AI to the profile