  Generating accurate digital tree models from scanned environments is
invaluable for forestry, agriculture, and other outdoor industries in tasks
such as identifying biomass, fall hazards and traversability, as well as
digital applications such as animation and gaming. Existing methods for tree
reconstruction rely on feature identification (trunk, crown, etc) to
heuristically segment a forest into individual trees and generate a branch
structure graph, limiting their application to sparse 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 support wind-loading. 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. Despite the
high variability of datasets containing trees, and the high rate of occlusions,
our method generates detailed and accurate tree structures in most cases.
