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

Abstract

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.

Code and data availability

The supplied blocks describe the paper's maize, synthetic tree, and Komatsuna experiments but contain no public deposit of the authors' point cloud data, code, or models. The Komatsuna RGB-D dataset is cited prior work [USM*17], not a paper-specific asset. The only supporting information mentioned is a video (Data S1),

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