Tree Reconstruction Using Topology Optimisation

Abstract

Generating accurate digital tree models from scanned environments is invaluable for forestry, agriculture, and other outdoor industries in tasks such as identifying fall hazards, estimating trees’ biomass and calculating traversability. Existing methods for tree reconstruction rely on sparse feature identification to segment a forest into individual trees and generate a branch structure graph, limiting their application to easily separable trees and uniform forests. However, the natural world is a messy place in which trees present with significant heterogeneity and are frequently encroached upon by the surrounding environment. We present a general method for extracting the branch structure of trees from point cloud data, which estimates the structure of trees by adapting the methods of structural topology optimisation to find the optimal material distribution to interpolate the input data. We present the results of this optimisation over a wide variety of scans, and discuss the benefits and drawbacks of this novel approach to tree structure reconstruction. Our method generates detailed and accurate tree structures, with a mean Surface Error (SE) of 15 cm over 13 diverse tree datasets.

Code and data availability

The paper's own tree point-cloud datasets are declared available at a CSIRO repository (https://doi.org/10.25919/yt2m-9373), but that URL is not among the allowed_urls, so it cannot be cited as an actionable asset. The GitHub repositories mentioned (TreeQSM, PANSFEM2, TreeSeparation) are third-party/cited tools, not a

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