Approach for modeling single branches of meadow orchard trees with 3D
point clouds
- URL: http://arxiv.org/abs/2104.05282v1
- Date: Mon, 12 Apr 2021 08:25:27 GMT
- Title: Approach for modeling single branches of meadow orchard trees with 3D
point clouds
- Authors: Jonas Straub, David Reiser and Hans W. Griepentrog
- Abstract summary: The cultivation of orchard meadows provides an ecological benefit for biodiversity, which is significantly higher than in intensively cultivated orchards.
The goal of this research is to create a tree model to automatically determine possible pruning points for stand-alone trees within meadows.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The cultivation of orchard meadows provides an ecological benefit for
biodiversity, which is significantly higher than in intensively cultivated
orchards. The goal of this research is to create a tree model to automatically
determine possible pruning points for stand-alone trees within meadows. The
algorithm which is presented here is capable of building a skeleton model based
on a pre-segmented photogrammetric 3D point cloud. Good results were achieved
in assigning the points to their leading branches and building a virtual tree
model, reaching an overall accuracy of 95.19 %. This model provided the
necessary information about the geometry of the tree for automated pruning.
Related papers
- BranchPoseNet: Characterizing tree branching with a deep learning-based pose estimation approach [0.0]
This paper presents an automated pipeline for detecting tree whorls in proximally laser scanning data using a pose-estimation deep learning model.
Accurate whorl detection provides valuable insights into tree growth patterns, wood quality, and offers potential for use as a biometric marker to track trees throughout the forestry value chain.
arXiv Detail & Related papers (2024-09-23T07:10:11Z) - Forecasting with Hyper-Trees [50.72190208487953]
Hyper-Trees are designed to learn the parameters of time series models.
By relating the parameters of a target time series model to features, Hyper-Trees also address the issue of parameter non-stationarity.
In this novel approach, the trees first generate informative representations from the input features, which a shallow network then maps to the target model parameters.
arXiv Detail & Related papers (2024-05-13T15:22:15Z) - 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) - CherryPicker: Semantic Skeletonization and Topological Reconstruction of
Cherry Trees [3.8697834534260447]
We present CherryPicker, an automatic pipeline that reconstructs photo-metric point clouds of trees.
Our system combines several state-of-the-art algorithms to enable automatic processing for further usage in 3D-plant phenotyping applications.
arXiv Detail & Related papers (2023-04-10T16:54:05Z) - RLET: A Reinforcement Learning Based Approach for Explainable QA with
Entailment Trees [47.745218107037786]
We propose RLET, a Reinforcement Learning based Entailment Tree generation framework.
RLET iteratively performs single step reasoning with sentence selection and deduction generation modules.
Experiments on three settings of the EntailmentBank dataset demonstrate the strength of using RL framework.
arXiv Detail & Related papers (2022-10-31T06:45:05Z) - Tree Reconstruction using Topology Optimisation [0.685316573653194]
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.
arXiv Detail & Related papers (2022-05-26T07:08:32Z) - Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner [56.08919422452905]
We propose an architecture called Iterative Retrieval-Generation Reasoner (IRGR)
Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises.
We outperform existing benchmarks on premise retrieval and entailment tree generation, with around 300% gain in overall correctness.
arXiv Detail & Related papers (2022-05-18T21:52:11Z) - A procedure for automated tree pruning suggestion using LiDAR scans of
fruit trees [0.0]
In fruit tree growth, pruning is an important management practice for preventing overcrowding, improving canopy access to light and promoting regrowth.
We present a framework for suggesting pruning strategies on LiDAR-scanned commercial fruit trees using a scoring function.
The light distribution was improved by up to 25.15%, demonstrating a 16% improvement over commercial pruning on a real tree, and certain cut points were discovered which improved light distribution with a smaller negative impact on tree volume.
arXiv Detail & Related papers (2021-02-07T02:18:56Z) - 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) - PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree
Conditions [66.87405921626004]
This paper investigates the novel problem of generating 3D shape point cloud geometry from a symbolic part tree representation.
We propose a conditional GAN "part tree"-to-"point cloud" model (PT2PC) that disentangles the structural and geometric factors.
arXiv Detail & Related papers (2020-03-19T08:27:25Z)
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.