Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods

• URL: http://arxiv.org/abs/2012.11489v1
• Date: Mon, 21 Dec 2020 17:01:50 GMT
• Title: Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods
• Abstract summary: This work introduces a benchmark for assessing the performance of 3D point-based deep learning methods on organ segmentation of 3D plant models. The methods were tested on the ROSE-X data set, containing fully annotated 3D models of real rosebush plants.
• Score: 2.384818913693926
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Segmentation of structural parts of 3D models of plants is an important step for plant phenotyping, especially for monitoring architectural and morphological traits. This work introduces a benchmark for assessing the performance of 3D point-based deep learning methods on organ segmentation of 3D plant models, specifically rosebush models. Six recent deep learning architectures that segment 3D point clouds into semantic parts were adapted and compared. The methods were tested on the ROSE-X data set, containing fully annotated 3D models of real rosebush plants. The contribution of incorporating synthetic 3D models generated through Lindenmayer systems into training data was also investigated.

Related papers

• Pano3D: Unified 3D Reconstruction and Panoptic Segmentation [68.18993232814061]
  We introduce an approach that performs 3D reconstruction and 3D panoptic segmentation in a unified framework.<n>We build on existing 3D reconstruction models and augment them with a set-based mask decoder.<n>Our method achieves state-of-the-art performance in 3D panoptic segmentation across ScanNet, ScanNet200, and ScanNet++ datasets.
  arXiv  Detail & Related papers  (2026-06-12T09:43:13Z)
• Towards scalable organ level 3D plant segmentation: Bridging the data algorithm computing gap [9.655034225644847]
  Plant phenotyping provides valuable insights into plant environment interactions and genetic evolution.<n>Despite progress in general 3D computer vision domains, the adoption of 3D segmentation for plant phenotyping remains limited by three major challenges.<n>This study bridges the gap between algorithmic advances and practical deployment.
  arXiv  Detail & Related papers  (2025-09-08T04:21:27Z)
• Graph-Based Deep Learning for Component Segmentation of Maize Plants [0.0]
  We propose a novel Deep Learning architecture to detect components of individual plants on LiDAR 3D Point Cloud data sets.<n>This architecture is based on the concept of Graph Neural Networks (GNN) and feature enhancing with Principal Component Analysis (PCA)<n>Our graph-based deep learning approach enhances segmentation accuracy for identifying individual plant components, achieving percentages above 80% in the IoU average.
  arXiv  Detail & Related papers  (2025-06-30T18:44:27Z)
• Learning to Infer Parameterized Representations of Plants from 3D Scans [2.6774002558989705]
  We train a neural network with virtual plants generated using an L-systems-based procedural model.<n>After training, the network allows to infer a parametric tree-like representation based on an input 3D point cloud.<n>We evaluate our approach on Chenopodium Album plants, using experiments on synthetic plants to show that our unified framework allows for different tasks.
  arXiv  Detail & Related papers  (2025-05-28T13:23:48Z)
• PlantDreamer: Achieving Realistic 3D Plant Models with Diffusion-Guided Gaussian Splatting [0.7937206070844555]
  We introduce PlantDreamer, a novel approach to 3D synthetic plant generation.<n>It can achieve greater levels of realism for complex plant geometry and textures than available text-to-3D models.<n>We evaluate our approach by comparing its outputs with state-of-the-art text-to-3D models.
  arXiv  Detail & Related papers  (2025-05-21T13:51:57Z)
• CropCraft: Inverse Procedural Modeling for 3D Reconstruction of Crop Plants [16.558411700996746]
  We present a novel method for 3D reconstruction of agricultural crops based on optimizing a model of plant morphology via inverse procedural modeling. We validate our method on a dataset of real images of agricultural fields, and demonstrate that the reconstructions can be used for a variety of monitoring and simulation applications.
  arXiv  Detail & Related papers  (2024-11-14T18:58:02Z)
• PLANesT-3D: A new annotated dataset for segmentation of 3D plant point clouds [0.892439883048931]
  We introduce PLANesT-3D; a new annotated dataset of 3D color point clouds of plants. PLANesT-3D is composed of 34 point cloud models representing 34 real plants from three different plant species.
  arXiv  Detail & Related papers  (2024-07-30T19:27:37Z)
• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation [66.3814684757376]
  This work presents Zero123-6D, the first work to demonstrate the utility of Diffusion Model-based novel-view-synthesizers in enhancing RGB 6D pose estimation at category-level. The outlined method shows reduction in data requirements, removal of the necessity of depth information in zero-shot category-level 6D pose estimation task, and increased performance, quantitatively demonstrated through experiments on the CO3D dataset.
  arXiv  Detail & Related papers  (2024-03-21T10:38:18Z)
• Unsupervised 3D Pose Estimation with Non-Rigid Structure-from-Motion Modeling [83.76377808476039]
  We propose a new modeling method for human pose deformations and design an accompanying diffusion-based motion prior. Inspired by the field of non-rigid structure-from-motion, we divide the task of reconstructing 3D human skeletons in motion into the estimation of a 3D reference skeleton. A mixed spatial-temporal NRSfMformer is used to simultaneously estimate the 3D reference skeleton and the skeleton deformation of each frame from 2D observations sequence.
  arXiv  Detail & Related papers  (2023-08-18T16:41:57Z)
• Enforcing 3D Topological Constraints in Composite Objects via Implicit Functions [60.56741715207466]
  Medical applications often require accurate 3D representations of complex organs with multiple parts, such as the heart and spine. This paper introduces a novel approach to enforce topological constraints in 3D object reconstruction using deep implicit signed distance functions. We propose a sampling-based technique that effectively checks and enforces topological constraints between 3D shapes by evaluating signed distances at randomly sampled points throughout the volume.
  arXiv  Detail & Related papers  (2023-07-16T10:07:15Z)
• 3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset [19.7595986056387]
  3D dense reconstruction refers to the process of obtaining the complete shape and texture features of 3D objects from 2D planar images. This work systematically introduces classical methods of 3D dense reconstruction based on geometric and optical models. It also introduces datasets for deep learning and the performance and advantages and disadvantages demonstrated by deep learning methods on these datasets.
  arXiv  Detail & Related papers  (2023-04-19T01:56:55Z)
• Using t-distributed stochastic neighbor embedding for visualization and segmentation of 3D point clouds of plants [0.0]
  t-SNE is proposed to embed 3D point clouds of plants into 2D space for plant characterization. The perplexity parameter of t-SNE allows 2D rendering of plant structures at various organizational levels.
  arXiv  Detail & Related papers  (2023-02-07T12:55:15Z)
• Eff-3DPSeg: 3D organ-level plant shoot segmentation using annotation-efficient point clouds [1.5882586857953638]
  We propose a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. High-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system. A weakly-supervised deep learning method was proposed for plant organ segmentation.
  arXiv  Detail & Related papers  (2022-12-20T14:09:37Z)
• Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis [143.22192229456306]
  This paper proposes a deep 3D energy-based model to represent volumetric shapes. The benefits of the proposed model are six-fold. Experiments demonstrate that the proposed model can generate high-quality 3D shape patterns.
  arXiv  Detail & Related papers  (2020-12-25T06:09:36Z)
• Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation [87.54570024320354]
  State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space. We develop a 3D cylinder partition and a 3D cylinder convolution based framework, termed as Cylinder3D, which exploits the 3D topology relations and structures of driving-scene point clouds.
  arXiv  Detail & Related papers  (2020-08-04T13:56:19Z)
• Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image [102.44347847154867]
  We propose a novel formulation that allows to jointly recover the geometry of a 3D object as a set of primitives. Our model recovers the higher level structural decomposition of various objects in the form of a binary tree of primitives. Our experiments on the ShapeNet and D-FAUST datasets demonstrate that considering the organization of parts indeed facilitates reasoning about 3D geometry.
  arXiv  Detail & Related papers  (2020-04-02T17:58:05Z)

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.