Three-dimentional reconstruction of complex, dynamic population canopy architecture for crops with a novel point cloud completion model: A case study in Brassica napus rapeseed

• URL: http://arxiv.org/abs/2506.18292v2
• Date: Fri, 01 Aug 2025 03:35:41 GMT
• Title: Three-dimentional reconstruction of complex, dynamic population canopy architecture for crops with a novel point cloud completion model: A case study in Brassica napus rapeseed
• Authors: Ziyue Guo, Xin Yang, Yutao Shen, Yang Zhu, Lixi Jiang, Haiyan Cen,
• Abstract summary: We propose an effective method for 3D reconstruction of complex, dynamic population canopy architecture for rapeseed crops.<n>A complete point cloud generation framework was developed for automated annotation of the training dataset.<n>The crop population point cloud completion network (CP-PCN) was then designed with a multi-resolution dynamic graph convolutional encoder (MRDG) and a point pyramid decoder (PPD) to predict occluded points.
• Score: 4.377318975816766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantitative descriptions of the complete canopy architecture are essential for accurately evaluating crop photosynthesis and yield performance to guide ideotype design. Although various sensing technologies have been developed for three-dimensional (3D) reconstruction of individual plants and canopies, they failed to obtain an accurate description of canopy architectures due to severe occlusion among complex canopy architectures. We proposed an effective method for 3D reconstruction of complex, dynamic population canopy architecture for rapeseed crops with a novel point cloud completion model. A complete point cloud generation framework was developed for automated annotation of the training dataset by distinguishing surface points from occluded points within canopies. The crop population point cloud completion network (CP-PCN) was then designed with a multi-resolution dynamic graph convolutional encoder (MRDG) and a point pyramid decoder (PPD) to predict occluded points. To further enhance feature extraction, a dynamic graph convolutional feature extractor (DGCFE) module was proposed to capture structural variations over the whole rapeseed growth period. The results demonstrated that CP-PCN achieved chamfer distance (CD) values of 3.35 cm -4.51 cm over four growth stages, outperforming the state-of-the-art transformer-based method (PoinTr). Ablation studies confirmed the effectiveness of the MRDG and DGCFE modules. Moreover, the validation experiment demonstrated that the silique efficiency index developed from CP-PCN improved the overall accuracy of rapeseed yield prediction by 11.2% compared to that of using incomplete point clouds. The CP-PCN pipeline has the potential to be extended to other crops, significantly advancing the quantitatively analysis of in-field population canopy architectures.

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)
• A Novel WaveInst-based Network for Tree Trunk Structure Extraction and Pattern Analysis in Forest Inventory [2.793797265684592]
  This work proposes a novel WaveInst instance segmentation framework, involving a discrete wavelet transform, to improve tree structure extraction.<n> Experimental results of the proposed model show superior performance on SynthTree43k, CaneTree100, Urban Street and our PoplarDataset.<n>The proposed method achieves a mean average precision of 49.6 and 24.3 for the structure extraction of mature and juvenile trees, respectively, surpassing the existing state-of-the-art method by 9.9.
  arXiv  Detail & Related papers  (2025-05-03T02:19:55Z)
• PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport [56.56430888985025]
  We propose a generative adversarial network for point cloud quality enhancement (PCE-GAN)<n>The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit.<n>The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality reconstruction.
  arXiv  Detail & Related papers  (2025-02-26T07:34:33Z)
• GP-GS: Gaussian Processes for Enhanced Gaussian Splatting [15.263608848427136]
  This paper proposes a novel 3D reconstruction framework, Gaussian Processes enhanced Gaussian Splatting (GP-GS)<n>GP-GS enables adaptive and uncertainty-guided densification of sparse Structure-from-Motion point clouds.<n>Experiments conducted on synthetic and real-world datasets validate the effectiveness and practicality of the proposed framework.
  arXiv  Detail & Related papers  (2025-02-04T12:50:16Z)
• ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based Upsampling [0.0]
  ArchComplete is a two-stage voxel-based 3D generative pipeline consisting of a vector-quantised model.<n>Key to our pipeline is (i) learning a contextually rich codebook of local patch embeddings, optimised alongside a 2.5D perceptual loss.<n> ArchComplete autoregressively generates models at the resolution of $643$ and progressively refines them up to $5123$, with voxel sizes as small as $ approx 9textcm$.
  arXiv  Detail & Related papers  (2024-12-23T20:13:27Z)
• PointRWKV: Efficient RWKV-Like Model for Hierarchical Point Cloud Learning [56.14518823931901]
  We present PointRWKV, a model of linear complexity derived from the RWKV model in the NLP field. We first propose to explore the global processing capabilities within PointRWKV blocks using modified multi-headed matrix-valued states. To extract local geometric features simultaneously, we design a parallel branch to encode the point cloud efficiently in a fixed radius near-neighbors graph with a graph stabilizer.
  arXiv  Detail & Related papers  (2024-05-24T05:02:51Z)
• 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)
• Dynamic 3D Point Cloud Sequences as 2D Videos [81.46246338686478]
  3D point cloud sequences serve as one of the most common and practical representation modalities of real-world environments. We propose a novel generic representation called textitStructured Point Cloud Videos (SPCVs) SPCVs re-organizes a point cloud sequence as a 2D video with spatial smoothness and temporal consistency, where the pixel values correspond to the 3D coordinates of points.
  arXiv  Detail & Related papers  (2024-03-02T08:18:57Z)
• Variational Relational Point Completion Network for Robust 3D Classification [59.80993960827833]
  Vari point cloud completion methods tend to generate global shape skeletons hence lack fine local details. This paper proposes a variational framework, point Completion Network (VRCNet) with two appealing properties. VRCNet shows great generalizability and robustness on real-world point cloud scans.
  arXiv  Detail & Related papers  (2023-04-18T17:03:20Z)
• 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)
• PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths [60.32185890237936]
  We design a novel neural network, named PMP-Net++, to mimic behavior of an earth mover. It moves each point of incomplete input to obtain a complete point cloud, where total distance of point moving paths (PMPs) should be the shortest. The network learns a strict and unique correspondence on point-level, and thus improves quality of predicted complete shape.
  arXiv  Detail & Related papers  (2022-02-19T03:00:40Z)
• Reconstructing Compact Building Models from Point Clouds Using Deep Implicit Fields [4.683612295430956]
  We present a novel framework for reconstructing compact, watertight, polygonal building models from point clouds. Experiments on both synthetic and real-world point clouds have demonstrated that, with our neural-guided strategy, high-quality building models can be obtained with significant advantages in fidelity, compactness, and computational efficiency.
  arXiv  Detail & Related papers  (2021-12-24T21:32:32Z)
• A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion [69.32451612060214]
  Real-scanned 3D point clouds are often incomplete, and it is important to recover complete point clouds for downstream applications. Most existing point cloud completion methods use Chamfer Distance (CD) loss for training. We propose a novel Point Diffusion-Refinement (PDR) paradigm for point cloud completion.
  arXiv  Detail & Related papers  (2021-12-07T06:59:06Z)
• PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths [54.459879603473034]
  We design a novel neural network, named PMP-Net, to mimic the behavior of an earth mover. It moves each point of the incomplete input to complete the point cloud, where the total distance of point moving paths should be shortest. It learns a strict and unique correspondence on point-level, which can capture the detailed topology and structure relationships between the incomplete shape and the complete target.
  arXiv  Detail & Related papers  (2020-12-07T01:34:38Z)

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.