Tree Reconstruction using Topology Optimisation
- URL: http://arxiv.org/abs/2205.13192v1
- Date: Thu, 26 May 2022 07:08:32 GMT
- Title: Tree Reconstruction using Topology Optimisation
- Authors: Thomas Lowe and Joshua Pinskier
- Abstract summary: We present a general method for extracting the branch structure of trees from point cloud data.
We discuss the benefits and drawbacks of this novel approach to tree structure reconstruction.
Our method generates detailed and accurate tree structures in most cases.
- Score: 0.685316573653194
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- Tree semantic segmentation from aerial image time series [24.14827064108217]
We perform semantic segmentation of trees using an aerial dataset image spanning over a year.
We compare models trained on single images versus those trained on time series to assess the impact of tree phenology on segmentation performances.
We leverage the hierarchical structure of tree species taxonomy by incorporating a custom loss function that refines predictions at three levels: species, genus, and higher-level taxa.
arXiv Detail & Related papers (2024-07-18T02:19:57Z) - Learning a Decision Tree Algorithm with Transformers [80.49817544396379]
We introduce MetaTree, which trains a transformer-based model on filtered outputs from classical algorithms to produce strong decision trees for classification.
We then train MetaTree to produce the trees that achieve strong generalization performance.
arXiv Detail & Related papers (2024-02-06T07:40:53Z) - Why do Random Forests Work? Understanding Tree Ensembles as
Self-Regularizing Adaptive Smoothers [68.76846801719095]
We argue that the current high-level dichotomy into bias- and variance-reduction prevalent in statistics is insufficient to understand tree ensembles.
We show that forests can improve upon trees by three distinct mechanisms that are usually implicitly entangled.
arXiv Detail & Related papers (2024-02-02T15:36:43Z) - Benchmarking Individual Tree Mapping with Sub-meter Imagery [6.907098367807166]
We introduce an evaluation framework suited for individual tree mapping in any physical environment.
We review and compare different approaches and deep architectures, and introduce a new method that we experimentally prove to be a good compromise between segmentation and detection.
arXiv Detail & Related papers (2023-11-14T08:21:36Z) - Tree Variational Autoencoders [5.992683455757179]
We propose a new generative hierarchical clustering model that learns a flexible tree-based posterior distribution over latent variables.
TreeVAE hierarchically divides samples according to their intrinsic characteristics, shedding light on hidden structures in the data.
arXiv Detail & Related papers (2023-06-15T09:25:04Z) - Hierarchical clustering with dot products recovers hidden tree structure [53.68551192799585]
In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure.
We recommend a simple variant of the standard algorithm, in which clusters are merged by maximum average dot product and not, for example, by minimum distance or within-cluster variance.
We demonstrate that the tree output by this algorithm provides a bona fide estimate of generative hierarchical structure in data, under a generic probabilistic graphical model.
arXiv Detail & Related papers (2023-05-24T11:05:12Z) - DeepTree: Modeling Trees with Situated Latents [8.372189962601073]
We propose a novel method for modeling trees based on learning developmental rules for branching structures instead of manually defining them.
We call our deep neural model situated latent because its behavior is determined by the intrinsic state.
Our method enables generating a wide variety of tree shapes without the need to define intricate parameters.
arXiv Detail & Related papers (2023-05-09T03:33:14Z) - Occlusion Reasoning for Skeleton Extraction of Self-Occluded Tree
Canopies [5.368313160283353]
A tree skeleton compactly describes the topological structure and contains useful information.
Our method uses an instance segmentation network to detect visible trunk, branches, and twigs.
We show that our method outperforms baseline methods in highly occluded scenes.
arXiv Detail & Related papers (2023-01-20T01:46:07Z) - Visualizing hierarchies in scRNA-seq data using a density tree-biased
autoencoder [50.591267188664666]
We propose an approach for identifying a meaningful tree structure from high-dimensional scRNA-seq data.
We then introduce DTAE, a tree-biased autoencoder that emphasizes the tree structure of the data in low dimensional space.
arXiv Detail & Related papers (2021-02-11T08:48:48Z) - Growing Deep Forests Efficiently with Soft Routing and Learned
Connectivity [79.83903179393164]
This paper further extends the deep forest idea in several important aspects.
We employ a probabilistic tree whose nodes make probabilistic routing decisions, a.k.a., soft routing, rather than hard binary decisions.
Experiments on the MNIST dataset demonstrate that our empowered deep forests can achieve better or comparable performance than [1],[3].
arXiv Detail & Related papers (2020-12-29T18:05:05Z) - MurTree: Optimal Classification Trees via Dynamic Programming and Search [61.817059565926336]
We present a novel algorithm for learning optimal classification trees based on dynamic programming and search.
Our approach uses only a fraction of the time required by the state-of-the-art and can handle datasets with tens of thousands of instances.
arXiv Detail & Related papers (2020-07-24T17:06:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.