CherryPicker: Semantic Skeletonization and Topological Reconstruction of Cherry Trees

• URL: http://arxiv.org/abs/2304.04708v2
• Date: Thu, 17 Aug 2023 15:05:41 GMT
• Title: CherryPicker: Semantic Skeletonization and Topological Reconstruction of Cherry Trees
• Authors: Lukas Meyer, Andreas Gilson, Oliver Scholz, Marc Stamminger
• Abstract summary: 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.
• Score: 3.8697834534260447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In plant phenotyping, accurate trait extraction from 3D point clouds of trees is still an open problem. For automatic modeling and trait extraction of tree organs such as blossoms and fruits, the semantically segmented point cloud of a tree and the tree skeleton are necessary. Therefore, we present CherryPicker, an automatic pipeline that reconstructs photo-metric point clouds of trees, performs semantic segmentation and extracts their topological structure in form of a skeleton. Our system combines several state-of-the-art algorithms to enable automatic processing for further usage in 3D-plant phenotyping applications. Within this pipeline, we present a method to automatically estimate the scale factor of a monocular reconstruction to overcome scale ambiguity and obtain metrically correct point clouds. Furthermore, we propose a semantic skeletonization algorithm build up on Laplacian-based contraction. We also show by weighting different tree organs semantically, our approach can effectively remove artifacts induced by occlusion and structural size variations. CherryPicker obtains high-quality topology reconstructions of cherry trees with precise details.

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)
• Terminating Differentiable Tree Experts [77.2443883991608]
  We propose a neuro-symbolic Differentiable Tree Machine that learns tree operations using a combination of transformers and Representation Products. We first remove a series of different transformer layers that are used in every step by introducing a mixture of experts. We additionally propose a new termination algorithm to provide the model the power to choose how many steps to make automatically.
  arXiv  Detail & Related papers  (2024-07-02T08:45:38Z)
• 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)
• Smart-Tree: Neural Medial Axis Approximation of Point Clouds for 3D Tree Skeletonization [0.0]
  Smart-Tree is a supervised method for approximating the medial axes of branch skeletons from a tree point cloud. A greedy algorithm performs robust skeletonization using the estimated medial axis. We evaluate Smart-Tree using a multi-species synthetic tree dataset and perform qualitative analysis on a real-world tree point cloud.
  arXiv  Detail & Related papers  (2023-03-21T03:03:46Z)
• 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)
• Approach for modeling single branches of meadow orchard trees with 3D point clouds [0.0]
  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.
  arXiv  Detail & Related papers  (2021-04-12T08:25:27Z)
• 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)
• SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples [75.32013242448151]
  We consider learning Ising tree models when the observations from the nodes are corrupted by independent but non-identically distributed noise. Katiyar et al. (2020) showed that although the exact tree structure cannot be recovered, one can recover a partial tree structure. We propose Symmetrized Geometric Averaging (SGA), a more statistically robust algorithm for partial tree recovery.
  arXiv  Detail & Related papers  (2021-01-22T01:57:35Z)
• 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)
• 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.