PLANesT-3D: A new annotated dataset for segmentation of 3D plant point clouds

• URL: http://arxiv.org/abs/2407.21150v1
• Date: Tue, 30 Jul 2024 19:27:37 GMT
• Title: PLANesT-3D: A new annotated dataset for segmentation of 3D plant point clouds
• Authors: Kerem Mertoğlu, Yusuf Şalk, Server Karahan Sarıkaya, Kaya Turgut, Yasemin Evrenesoğlu, Hakan Çevikalp, Ömer Nezih Gerek, Helin Dutağacı, David Rousseau,
• Abstract summary: 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.
• Score: 0.892439883048931
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Creation of new annotated public datasets is crucial in helping advances in 3D computer vision and machine learning meet their full potential for automatic interpretation of 3D plant models. In this paper, 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: \textit{Capsicum annuum}, \textit{Rosa kordana}, and \textit{Ribes rubrum}. Both semantic labels in terms of "leaf" and "stem", and organ instance labels were manually annotated for the full point clouds. As an additional contribution, SP-LSCnet, a novel semantic segmentation method that is a combination of unsupervised superpoint extraction and a 3D point-based deep learning approach is introduced and evaluated on the new dataset. Two existing deep neural network architectures, PointNet++ and RoseSegNet were also tested on the point clouds of PLANesT-3D for semantic segmentation.

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)
• ParaPoint: Learning Global Free-Boundary Surface Parameterization of 3D Point Clouds [52.03819676074455]
  ParaPoint is an unsupervised neural learning pipeline for achieving global free-boundary surface parameterization. This work makes the first attempt to investigate neural point cloud parameterization that pursues both global mappings and free boundaries.
  arXiv  Detail & Related papers  (2024-03-15T14:35:05Z)
• 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)
• SNAKE: Shape-aware Neural 3D Keypoint Field [62.91169625183118]
  Detecting 3D keypoints from point clouds is important for shape reconstruction. This work investigates the dual question: can shape reconstruction benefit 3D keypoint detection? We propose a novel unsupervised paradigm named SNAKE, which is short for shape-aware neural 3D keypoint field.
  arXiv  Detail & Related papers  (2022-06-03T17:58:43Z)
• MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention Point Generator [33.354908372755325]
  This work proposes a novel autolabeler, called multimodal attention point generator (MAP-Gen), that generates high-quality 3D labels from weak 2D boxes. Using MAP-Gen, object detection networks that are weakly supervised by 2D boxes can achieve 9499% performance of those fully supervised by 3D annotations.
  arXiv  Detail & Related papers  (2022-03-29T16:02:16Z)
• PointAttN: You Only Need Attention for Point Cloud Completion [89.88766317412052]
  Point cloud completion refers to completing 3D shapes from partial 3D point clouds. We propose a novel neural network for processing point cloud in a per-point manner to eliminate kNNs. The proposed framework, namely PointAttN, is simple, neat and effective, which can precisely capture the structural information of 3D shapes.
  arXiv  Detail & Related papers  (2022-03-16T09:20:01Z)
• Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception [122.53774221136193]
  State-of-the-art methods for driving-scene LiDAR-based perception often project the point clouds to 2D space and then process them via 2D convolution. A natural remedy is to utilize the 3D voxelization and 3D convolution network. We propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern.
  arXiv  Detail & Related papers  (2021-09-12T06:25:11Z)
• SCSS-Net: Superpoint Constrained Semi-supervised Segmentation Network for 3D Indoor Scenes [6.3364439467281315]
  We propose a superpoint constrained semi-supervised segmentation network for 3D point clouds, named as SCSS-Net. Specifically, we use the pseudo labels predicted from unlabeled point clouds for self-training, and the superpoints produced by geometry-based and color-based Region Growing algorithms are combined to modify and delete pseudo labels with low confidence.
  arXiv  Detail & Related papers  (2021-07-08T04:43:21Z)
• 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)
• ODFNet: Using orientation distribution functions to characterize 3D point clouds [0.0]
  We leverage on point orientation distributions around a point in order to obtain an expressive local neighborhood representation for point clouds. New ODFNet model achieves state-of-the-art accuracy for object classification on ModelNet40 and ScanObjectNN datasets.
  arXiv  Detail & Related papers  (2020-12-08T19:54:20Z)
• Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds [49.47017280475232]
  We study an unsupervised domain adaptation problem for the semantic labeling of 3D point clouds. We take a Complete and Label approach to recover the underlying surfaces before passing them to a segmentation network. The recovered 3D surfaces serve as a canonical domain, from which semantic labels can transfer across different LiDAR sensors.
  arXiv  Detail & Related papers  (2020-07-16T17:42:05Z)
• Pointwise Attention-Based Atrous Convolutional Neural Networks [15.499267533387039]
  A pointwise attention-based atrous convolutional neural network architecture is proposed to efficiently deal with a large number of points. The proposed model has been evaluated on the two most important 3D point cloud datasets for the 3D semantic segmentation task. It achieves a reasonable performance compared to state-of-the-art models in terms of accuracy, with a much smaller number of parameters.
  arXiv  Detail & Related papers  (2019-12-27T13:12:58Z)

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.