MaizeField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity Panel

• URL: http://arxiv.org/abs/2503.07813v3
• Date: Thu, 03 Jul 2025 16:22:27 GMT
• Title: MaizeField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity Panel
• Authors: Elvis Kimara, Mozhgan Hadadi, Jackson Godbersen, Aditya Balu, Talukder Jubery, Yawei Li, Adarsh Krishnamurthy, Patrick S. Schnable, Baskar Ganapathysubramanian,
• Abstract summary: We present a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel.<n>MaizeField3D will serve as a foundational dataset for AI-driven phenotyping, plant structural analysis, and 3D applications in agricultural research.
• Score: 12.89812013060155
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The development of artificial intelligence (AI) and machine learning (ML) based tools for 3D phenotyping, especially for maize, has been limited due to the lack of large and diverse 3D datasets. 2D image datasets fail to capture essential structural details such as leaf architecture, plant volume, and spatial arrangements that 3D data provide. To address this limitation, we present MaizeField3D (https://baskargroup.github.io/MaizeField3D/), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be AI-ready for advancing agricultural research. Our dataset includes 1,045 high-quality point clouds of field-grown maize collected using a terrestrial laser scanner (TLS). Point clouds of 520 plants from this dataset were segmented and annotated using a graph-based segmentation method to isolate individual leaves and stalks, ensuring consistent labeling across all samples. This labeled data was then used for fitting procedural models that provide a structured parametric representation of the maize plants. The leaves of the maize plants in the procedural models are represented using Non-Uniform Rational B-Spline (NURBS) surfaces that were generated using a two-step optimization process combining gradient-free and gradient-based methods. We conducted rigorous manual quality control on all datasets, correcting errors in segmentation, ensuring accurate leaf ordering, and validating metadata annotations. The dataset also includes metadata detailing plant morphology and quality, alongside multi-resolution subsampled point cloud data (100k, 50k, 10k points), which can be readily used for different downstream computational tasks. MaizeField3D will serve as a comprehensive foundational dataset for AI-driven phenotyping, plant structural analysis, and 3D applications in agricultural research.

Related papers

• 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)
• MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling [0.0]
  Fine-tuning large generative models is a promising perspective for making these models available in fields like engineering. We present MeshFleet, a filtered and annotated 3D dataset extracted from XL, the most extensive publicly available collection of 3D objects. We demonstrate the efficacy of our filtering method through a comparative analysis against caption and image aesthetic score-based techniques and fine-tuning experiments with SV3D.
  arXiv  Detail & Related papers  (2025-03-18T08:09:24Z)
• Procedural Generation of 3D Maize Plant Architecture from LIDAR Data [16.458252508124794]
  This study introduces a robust framework for generating procedural 3D models of maize (Zea mays) plants from LiDAR point cloud data.<n>Our framework leverages Non-Uniform Rational B-Spline (NURBS) surfaces to model the leaves of maize plants.
  arXiv  Detail & Related papers  (2025-01-21T22:53:09Z)
• CULTURE3D: Cultural Landmarks and Terrain Dataset for 3D Applications [11.486451047360248]
  We present a large-scale fine-grained dataset using high-resolution images captured from locations worldwide.<n>Our dataset is built using drone-captured aerial imagery, which provides a more accurate perspective for capturing real-world site layouts and architectural structures.<n>The dataset enables seamless integration with multi-modal data, supporting a range of 3D applications, from architectural reconstruction to virtual tourism.
  arXiv  Detail & Related papers  (2025-01-12T20:36:39Z)
• 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)
• MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations [55.022519020409405]
  This paper builds the first largest ever multi-modal 3D scene dataset and benchmark with hierarchical grounded language annotations, MMScan.<n>The resulting multi-modal 3D dataset encompasses 1.4M meta-annotated captions on 109k objects and 7.7k regions as well as over 3.04M diverse samples for 3D visual grounding and question-answering benchmarks.
  arXiv  Detail & Related papers  (2024-06-13T17:59:30Z)
• Outdoor Scene Extrapolation with Hierarchical Generative Cellular Automata [70.9375320609781]
  We aim to generate fine-grained 3D geometry from large-scale sparse LiDAR scans, abundantly captured by autonomous vehicles (AV) We propose hierarchical Generative Cellular Automata (hGCA), a spatially scalable 3D generative model, which grows geometry with local kernels following, in a coarse-to-fine manner, equipped with a light-weight planner to induce global consistency.
  arXiv  Detail & Related papers  (2024-06-12T14:56:56Z)
• LAESI: Leaf Area Estimation with Synthetic Imagery [13.145253458335464]
  We introduce LAESI, a Synthetic Leaf dataset of 100,000 synthetic leaf images on millimeter paper. This dataset provides a resource for leaf morphology analysis aimed at beech and oak leaves. We evaluate the applicability of the dataset by training machine learning models for leaf surface area prediction and semantic segmentation.
  arXiv  Detail & Related papers  (2024-03-31T07:56:07Z)
• Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
  This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
  arXiv  Detail & Related papers  (2024-03-27T08:42:47Z)
• Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability [118.26563926533517]
  Auto-regressive models have achieved impressive results in 2D image generation by modeling joint distributions in grid space. We extend auto-regressive models to 3D domains, and seek a stronger ability of 3D shape generation by improving auto-regressive models at capacity and scalability simultaneously.
  arXiv  Detail & Related papers  (2024-02-19T15:33:09Z)
• DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance Fields [68.94868475824575]
  This paper introduces a novel approach capable of generating infinite, high-quality 3D-consistent 2D annotations alongside 3D point cloud segmentations. We leverage the strong semantic prior within a 3D generative model to train a semantic decoder. Once trained, the decoder efficiently generalizes across the latent space, enabling the generation of infinite data.
  arXiv  Detail & Related papers  (2023-11-18T21:58:28Z)
• Multi-Modal Dataset Acquisition for Photometrically Challenging Object [56.30027922063559]
  This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging objects. We propose a novel annotation and acquisition pipeline that enhances existing 3D perception and 6D object pose datasets.
  arXiv  Detail & Related papers  (2023-08-21T10:38:32Z)
• UniG3D: A Unified 3D Object Generation Dataset [75.49544172927749]
  UniG3D is a unified 3D object generation dataset constructed by employing a universal data transformation pipeline on ShapeNet datasets. This pipeline converts each raw 3D model into comprehensive multi-modal data representation. The selection of data sources for our dataset is based on their scale and quality.
  arXiv  Detail & Related papers  (2023-06-19T07:03:45Z)
• Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats [80.12253291709673]
  We propose a novel affine-combining autoencoder (ACAE) method to perform dimensionality reduction on the number of landmarks. Our approach scales to an extreme multi-dataset regime, where we use 28 3D human pose datasets to supervise one model.
  arXiv  Detail & Related papers  (2022-12-29T22:22:49Z)
• Geometry-Contrastive Transformer for Generalized 3D Pose Transfer [95.56457218144983]
  The intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. We propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies. We present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task.
  arXiv  Detail & Related papers  (2021-12-14T13:14:24Z)
• H3D: Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and textured Meshes from UAV LiDAR and Multi-View-Stereo [4.263987603222371]
  This paper introduces a 3D dataset which is unique in three ways. It depicts the village of Hessigheim (Germany) henceforth referred to as H3D. It is designed for promoting research in the field of 3D data analysis on one hand and to evaluate and rank emerging approaches.
  arXiv  Detail & Related papers  (2021-02-10T09:33:48Z)
• Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene [76.4183572058063]
  We present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks. The dataset has been point-wisely annotated with both hierarchical and instance-based labels. We formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
  arXiv  Detail & Related papers  (2020-08-11T19:10:32Z)

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.