A graph-based approach for simultaneous semantic and instance segmentation of plant 3D point clouds

Mirande, Katia; Godin, Christophe; Tisserand, Marie; Charlaix, Julie; Besnard, Fabrice; Hétroy-Wheeler, Franck

doi:10.3389/fpls.2022.1012669

Cited by 8 publications

(2 citation statements)

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“…Semantic segmentation based on deep learning has a very broad development prospect in the field of computer vision. However, many network models with good segmentation results occupy a large amount of memory and take a long time to process 3D point clouds (Mirande et al, 2022). Based on the DGCNN-sparse-dense point clouds mapping, it has faster processing speed and better segmentation results, and consumes less memory.…”

Section: Experimental Segmentation Of Silique Point Cloudsmentioning

confidence: 99%

Point clouds segmentation of rapeseed siliques based on sparse-dense point clouds mapping

et al. 2023

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In this study, we propose a high-throughput and low-cost automatic detection method based on deep learning to replace the inefficient manual counting of rapeseed siliques. First, a video is captured with a smartphone around the rapeseed plants in the silique stage. Feature point detection and matching based on SIFT operators are applied to the extracted video frames, and sparse point clouds are recovered using epipolar geometry and triangulation principles. The depth map is obtained by calculating the disparity of the matched images, and the dense point cloud is fused. The plant model of the whole rapeseed plant in the silique stage is reconstructed based on the structure-from-motion (SfM) algorithm, and the background is removed by using the passthrough filter. The downsampled 3D point cloud data is processed by the DGCNN network, and the point cloud is divided into two categories: sparse rapeseed canopy siliques and rapeseed stems. The sparse canopy siliques are then segmented from the original whole rapeseed siliques point cloud using the sparse-dense point cloud mapping method, which can effectively save running time and improve efficiency. Finally, Euclidean clustering segmentation is performed on the rapeseed canopy siliques, and the RANSAC algorithm is used to perform line segmentation on the connected siliques after clustering, obtaining the three-dimensional spatial position of each silique and counting the number of siliques. The proposed method was applied to identify 1457 siliques from 12 rapeseed plants, and the experimental results showed a recognition accuracy greater than 97.80%. The proposed method achieved good results in rapeseed silique recognition and provided a useful example for the application of deep learning networks in dense 3D point cloud segmentation.

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Section: Experimental Segmentation Of Silique Point Cloudsmentioning

confidence: 99%

Point clouds segmentation of rapeseed siliques based on sparse-dense point clouds mapping

et al. 2023

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“…Utilizing machine vision techniques to acquire image-based 3D models of seedlings offers several advantages. It eliminates the need for strict research environments, mitigates costly expenses, and enables the capture of color, texture information, and improved measurement precision [26,27]. Sun et al [28] proposed a high-throughput, three-dimensional rapid plant point cloud reconstruction method based on autonomous calibration of the Kinect v2 sensor position.…”

Section: Introductionmentioning

confidence: 99%

Research on Morphological Indicator Extraction Method of Pinus massoniana Lamb. Based on 3D Reconstruction

Li,

Xia,

Liu

et al. 2023

Forests

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Pinus massoniana (Lamb.) is an important plantation species in southern China. Accurate measurement of P. massoniana seedling morphological indicators is crucial for accelerating seedling quality assessment. Machine vision, with its objectivity and stability, can replace human eyes in performing these measurements. In this paper, a measurement method for seedling morphological indicators based on Euclidean distance, Laplacian contraction, PointNet++, and 3D reconstruction is proposed. Firstly, multi-angle sequence images of 30 one-year-old P. massoniana seedlings were collected, distorted, and corrected to generate a sparse point cloud through the Structure-from-Motion (SFM) and dense point cloud through the Patch-Based Multiple View Stereo (PMVS). Secondly, a Dense Weighted Semantic Segmentation Model based on PointNet++ was designed, achieving effective segmentation of the P. massoniana seedling point clouds. Finally, a multi-iteration plane method based on Laplacian contraction was proposed. The new skeleton points were refined by minimizing the Euclidean distance, iteratively generating the optimal morphological skeleton, thus facilitating the extraction of morphological indicators. The experimental results demonstrated a good correlation between the machine vision-extracted morphological indicators (including plant height, ground diameter, and height-to-diameter ratio) and manually measured data. The improved PointNet++ model achieved an accuracy of 0.9448 on the training set. The accuracy and Mean Intersection over Union (MIoU) of the test set reached 0.9430 and 0.7872, respectively. These findings can provide reliable technical references for the accurate assessment of P. massoniana seedling quality and the promotion of digital forestry construction.

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Improved 3D point cloud segmentation for accurate phenotypic analysis of cabbage plants using deep learning and clustering algorithms

Guo

Xie

Zhu

et al. 2023

Computers and Electronics in Agriculture

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A graph-based approach for simultaneous semantic and instance segmentation of plant 3D point clouds

Cited by 8 publications

References 50 publications

Point clouds segmentation of rapeseed siliques based on sparse-dense point clouds mapping

Point clouds segmentation of rapeseed siliques based on sparse-dense point clouds mapping

Research on Morphological Indicator Extraction Method of Pinus massoniana Lamb. Based on 3D Reconstruction

Improved 3D point cloud segmentation for accurate phenotypic analysis of cabbage plants using deep learning and clustering algorithms

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