Temporal Upsampling of Point Cloud Sequences by Optimal Transport for Plant Growth Visualization

In: Computer Graphics Forum (2020)
 

Abstract

Plant growth visualization from a series of 3D scanner measurements is a challenging task. Time intervals between successive measurements are typically too large to allow a smooth animation of the growth process. Therefore, obtaining a smooth animation of the plant growth process requires a temporal upsampling of the point cloud sequence in order to obtain approximations of the intermediate states between successive measurements. Additionally, there are suddenly arising structural changes due to the occurrence of new plant parts such as new branches or leaves. We present a novel method that addresses these challenges via semantic segmentation and the generation of a segment hierarchy per scan, the matching of the hierarchical representations of successive scans and the segment-wise computation of optimal transport. The transport problems' solutions yield the information required for a realistic temporal upsampling, which is generated in real-time. Thereby, our method does not require shape templates, good correspondences or huge databases of examples. Newly grown and decayed parts of the plant are detected as unmatched segments and are handled by identifying corresponding bifurcation points and introducing virtual segments in the previous, respectively successive time step. Our method allows the generation of realistic upsampled growth animations with moderate computational effort.

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Bibtex

@ARTICLE{GollaTemporalUpsamplingOptimalTransport2020,
    author = {Golla, Tim and Kneiphof, Tom and Kuhlmann, Heiner and Weinmann, Michael and Klein, Reinhard},
     title = {Temporal Upsampling of Point Cloud Sequences by Optimal Transport for Plant Growth Visualization},
   journal = {Computer Graphics Forum},
      year = {2020},
      note = {accepted},
  abstract = {Plant growth visualization from a series of 3D scanner measurements is a challenging task. Time
              intervals between successive measurements are typically too large to allow a smooth animation of the
              growth process. Therefore, obtaining a smooth animation of the plant growth process requires a
              temporal upsampling of the point cloud sequence in order to obtain approximations of the
              intermediate states between successive measurements. Additionally, there are suddenly arising
              structural changes due to the occurrence of new plant parts such as new branches or leaves. We
              present a novel method that addresses these challenges via semantic segmentation and the generation
              of a segment hierarchy per scan,  the matching of the hierarchical representations of successive
              scans and the segment-wise computation of optimal transport.   The transport problems' solutions
              yield the information required for a realistic temporal upsampling, which is generated in real-time.
              Thereby, our method does not require shape templates, good correspondences or huge databases of
              examples. Newly grown and decayed parts of the plant are detected as unmatched segments and are
              handled by identifying corresponding bifurcation points and introducing virtual segments in the
              previous, respectively successive time step. Our method allows the generation of realistic upsampled
              growth animations with moderate computational effort.},
       doi = {10.1111/cgf.14009}
}