scholarly journals Developing a Semantic-Driven Hybrid Segmentation Method for Point Clouds of 3D Shapes

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 40861-40880 ◽  
Author(s):  
Xiaowen Yang ◽  
Xie Han ◽  
Qingde Li ◽  
Ligang He ◽  
Min Pang ◽  
...  
Keyword(s):  
Point Clouds ◽  
Segmentation Method ◽  
Hybrid Segmentation ◽  
3D Shapes

scholarly journals Learning the Incremental Warp for 3D Vehicle Tracking in LiDAR Point Clouds

2021 ◽  
Vol 13 (14) ◽  
pp. 2770
Author(s):  
Shengjing Tian ◽  
Xiuping Liu ◽  
Meng Liu ◽  
Yuhao Bian ◽  
Junbin Gao ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Vehicle Tracking ◽  
Complex Nature ◽  
Global Feature ◽  
Tracking Accuracy ◽  
State Estimator ◽  
Latent Space ◽  
3D Shapes ◽  
Cloud Tracking

Object tracking from LiDAR point clouds, which are always incomplete, sparse, and unstructured, plays a crucial role in urban navigation. Some existing methods utilize a learned similarity network for locating the target, immensely limiting the advancements in tracking accuracy. In this study, we leveraged a powerful target discriminator and an accurate state estimator to robustly track target objects in challenging point cloud scenarios. Considering the complex nature of estimating the state, we extended the traditional Lucas and Kanade (LK) algorithm to 3D point cloud tracking. Specifically, we propose a state estimation subnetwork that aims to learn the incremental warp for updating the coarse target state. Moreover, to obtain a coarse state, we present a simple yet efficient discrimination subnetwork. It can project 3D shapes into a more discriminatory latent space by integrating the global feature into each point-wise feature. Experiments on KITTI and PandaSet datasets showed that compared with the most advanced of other methods, our proposed method can achieve significant improvements—in particular, up to 13.68% on KITTI.

scholarly journals A Fast, Fully Automated Prostate Boundary Segmentation Using Probabilistic Approaches In Ultrasound Images

2021 ◽  
Author(s):  
Rasa Vafaie
Keyword(s):  
Em Algorithm ◽  
Signal To Noise Ratio ◽  
Transrectal Ultrasound ◽  
Speckle Noise ◽  
Computational Time ◽  
Dice Similarity Coefficient ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Initial Model ◽  
Hybrid Segmentation

Segmentation of prostate boundaries in transrectal ultrasound (TRUS) images plays a great role in prostate cancer diagnosis. Due to the low signal to noise ratio and existence of the speckle noise in TRUS images, prostate image segmentation has proven to be an extremely difficult task. In this thesis report, a fast fully automated hybrid segmentation method based on probabilistic approaches is presented. First, the position of the initial model is automatically estimated using prostate boundary representative patterns. Next, the Expectation Maximization (EM) algorithm and Markov Random Field (MRF) theory are utilized in the deformation strategy to optimally fit the initial model on the prostate boundaries. A less computationally EM algorithm and a new surface smoothing technique are proposed to decrease the segmentation time. Successful experimental results with the average Dice Similarity Coefficient (DSC) value 93.9±2.7% and computational time around 9 seconds validate the algorithm.

scholarly journals Clustering-Based Plane Refitting of Non-planar Patches for Voxel-Based 3D Point Cloud Segmentation Using K-Means Clustering

2020 ◽  
Vol 37 (6) ◽  
pp. 1019-1027
Author(s):  
Ali Saglam ◽  
Hasan B. Makineci ◽  
Ömer K. Baykan ◽  
Nurdan Akhan Baykan
Keyword(s):  
Point Cloud ◽  
Point Group ◽  
Three Dimensional ◽  
Point Clouds ◽  
Segmentation Method ◽  
Train Station ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Point Cloud Segmentation ◽  
Geometric Cues

Point cloud processing is a struggled field because the points in the clouds are three-dimensional and irregular distributed signals. For this reason, the points in the point clouds are mostly sampled into regularly distributed voxels in the literature. Voxelization as a pretreatment significantly accelerates the process of segmenting surfaces. The geometric cues such as plane directions (normals) in the voxels are mostly used to segment the local surfaces. However, the sampling process may include a non-planar point group (patch), which is mostly on the edges and corners, in a voxel. These voxels can cause misleading the segmentation process. In this paper, we separate the non-planar patches into planar sub-patches using k-means clustering. The largest one among the planar sub-patches replaces the normal and barycenter properties of the voxel with those of itself. We have tested this process in a successful point cloud segmentation method and measure the effects of the proposed method on two point cloud segmentation datasets (Mosque and Train Station). The method increases the accuracy success of the Mosque dataset from 83.84% to 87.86% and that of the Train Station dataset from 85.36% to 87.07%.

scholarly journals ROBUST AND ACCURATE PLANE SEGMENTATION FROM POINT CLOUDS OF STRUCTURED SCENES

2020 ◽  
Vol V-2-2020 ◽  
pp. 221-226
Author(s):  
P. Hu ◽  
Y. Liu ◽  
M. Tian ◽  
M. Hou
Keyword(s):  
Energy Minimization ◽  
Point Cloud ◽  
Point Clouds ◽  
Segmentation Method ◽  
Topological Graph ◽  
New Approach ◽  
Input Point ◽  
Planar Surfaces ◽  
Point Density ◽  
Efficiency And Effectiveness

Abstract. Plane segmentation from the point cloud is an important step in various types of geo-information related to human activities. In this paper, we present a new approach to accurate segment planar primitives simultaneously by transforming it into the best matching issue between the over-segmented super-voxels and the 3D plane models. The super-voxels and its adjacent topological graph are firstly derived from the input point cloud as over-segmented small patches. Such initial 3D plane models are then enriched by fitting centroids of randomly sampled super-voxels, and translating these grouped planar super-voxels by structured scene prior (e.g. orthogonality, parallelism), while the generated adjacent graph will be updated along with planar clustering. To achieve the final super-voxels to planes assignment problem, an energy minimization framework is constructed using the productions of candidate planes, initial super-voxels, and the improved adjacent graph, and optimized to segment multiple consistent planar surfaces in the scenes simultaneously. The proposed algorithms are implemented, and three types of point clouds differing in feature characteristics (e.g. point density, complexity) are mainly tested to validate the efficiency and effectiveness of our segmentation method.

scholarly journals STRUCTURAL SEGMENTATION OF POINT CLOUDS WITH VARYING DENSITY BASED ON MULTI-SIZE SUPERVOXELS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 389-396
Author(s):  
Y. Li ◽  
B. Wu
Keyword(s):  
Laser Scanning ◽  
Large Data ◽  
Point Clouds ◽  
Semantic Interpretation ◽  
Minimum Size ◽  
Structural Components ◽  
Segmentation Method ◽  
Size Constraints ◽  
Point Density ◽  
Data Volume

<p><strong>Abstract.</strong> Ground objects can be regarded as a combination of structures of different geometries. Generally, the structural geometries can be grouped into linear, planar and scatter shapes. A good segmentation of objects into different structures can help to interpret the scanned scenes and provide essential clues for subsequent semantic interpretation. This is particularly true for the terrestrial static and mobile laser scanning data, where the geometric structures of objects are presented in detail due to the close scanning distances. In consideration of the large data volume and the large variation in point density of such point clouds, this paper presents a structural segmentation method of point clouds to efficiently decompose the ground objects into different structural components based on supervoxels of multiple sizes. First, supervoxels are generated with sizes adaptive to the point density with minimum occupied points and minimum size constraints. Then, the multi-size supervoxels are clustered into different components based on a structural labelling result obtained via Markov random field. Two datasets including terrestrial and mobile laser scanning point clouds were used to evaluate the performance of the proposed method. The results indicate that the proposed method can effectively and efficiently classify the point clouds into structurally meaningful segments with overall accuracies higher than 96%, even with largely varying point density.</p>

scholarly journals Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features

2020 ◽  
Vol 12 (7) ◽  
pp. 1078 ◽  
Author(s):  
Zhenyu Ma ◽  
Yong Pang ◽  
Di Wang ◽  
Xiaojun Liang ◽  
Bowei Chen ◽  
...  
Keyword(s):  
Region Growing ◽  
Point Clouds ◽  
Tree Canopy ◽  
Segmentation Method ◽  
Tree Crown ◽  
Two Stage ◽  
Individual Tree ◽  
Tree Structures ◽  
Larch Plantation

The detection of individual trees in a larch plantation could improve the management efficiency and production prediction. This study introduced a two-stage individual tree crown (ITC) segmentation method for airborne light detection and ranging (LiDAR) point clouds, focusing on larch plantation forests with different stem densities. The two-stage segmentation method consists of the region growing and morphology segmentation, which combines advantages of the region growing characteristics and the detailed morphology structures of tree crowns. The framework comprises five steps: (1) determination of the initial dominant segments using a region growing algorithm, (2) identification of segments to be redefined based on the 2D hull convex area of each segment, (3) establishment and selection of profiles based on the tree structures, (4) determination of the number of trees using the correlation coefficient of residuals between Gaussian fitting and the tree canopy shape described in each profile, and (5) k-means segmentation to obtain the point cloud of a single tree. The accuracy was evaluated in terms of correct matching, recall, precision, and F-score in eight plots with different stem densities. Results showed that the proposed method significantly increased ITC detections compared with that of using only the region growing algorithm, where the correct matching rate increased from 73.5% to 86.1%, and the recall value increased from 0.78 to 0.89.

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