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Unsupervised 3d object segmentation of Point Clouds by Geometry Consistency

IEEE TRANSACTIONS ON PATTERN ANALYSIS ANd MACHINE INTELLIGENCE 2024年第12期46卷 8459-8473页

作者： Song, Ziyang Yang, Bo Hong Kong Polytech Univ Shenzhen Res Inst VLAR Grp Hung Hom Hong Kong Peoples R China

In this paper, we study the problem of 3d object segmentation from raw point clouds. Unlike existing methods which usually require a large amount of human annotations for full supervision, we propose the first unsupervised method, called OGC, to simultaneously identify multiple 3d objects in a single forward pass, without needing any type of human annotations. The key to our approach is to fully leverage the dynamic motion patterns over sequential point clouds as supervision signals to automatically discover rigid objects. Our method consists of three major components, 1) the object segmentation network to directly estimate multi-object masks from a single point cloud frame, 2) the auxiliary self-supervised scene flow estimator, and 3) our core object geometry consistency component. By carefully designing a series of loss functions, we effectively take into account the multi-object rigid consistency and the object shape invariance in both temporal and spatial scales. This allows our method to truly discover the object geometry even in the absence of annotations. We extensively evaluate our method on five datasets, demonstrating the superior performance for object part instance segmentation and general object segmentation in both indoor and the challenging outdoor scenarios.

关键词： Point cloud compression Three-dimensional displays object segmentation Motion segmentation Annotations Geometry Vectors 3d object segmentation point cloud analysis scene flow unsupervised learning

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Large-scale 3d object segmentation employing conditional angular clustering technique

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MOdERN PHYSICS LETTERS B 2023年第17期37卷

作者： Nguyen, Thanh-Hung Pham, duc-An Nguyen, Xuan-Thuan Hanoi Univ Sci & Technol Sch Mech Engn Hanoi Vietnam

This paper aims to develop an automatic 3d object segmentation method for the large-scale point clouds. Given a range image, the preprocessing is first applied to get the optimal 3d point cloud. A k-nearest neighbor is built, and a segmentation algorithm based on the conditional angular clustering technique is used to segment the objects from the point cloud. The algorithm is tested on the real point cloud datasets. The experiment results demonstrated that the developed segmentation method can be used to localize the object with the relative uncertainty of 0.27%.

关键词： 3d object segmentation large-scale point cloud angular clustering technique

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3d object Classification and segmentation from Large Scale Area Reconstruction 14

3D Object Classification and Segmentation from Large Scale A...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Kim, Hye Sun Ban, Yun Ji Park, Chang Joon Won Kim, Ho Etri CG/Vision Research Lab. Daejeon Korea Republic of

ISBN: (纸本)9798350313277

3d reconstruction is difficult to use in general applications because it treats objects as a whole without distinction. Post-processing to segment them into individual objects is essential. However, when the reconstruction target is a large-scale area, it is difficult to segment directly from the 3d geometry because it is divided into small rectangular tiles for convenience. We propose a method that can accurately classify and segment objects even in the case of locally divided 3d tiled geometry. Instead of using 3d segmentation, we adopt the method of 2d instance segmentation of a global multi-view image and then projecting it onto the 3d geometry. This solves the problem that objects located on the boundaries of tiles are fragmented and not properly segmented. © 2023 IEEE.

关键词： 3d object segmentation large-scale reconstruction multi-view reconstruction object classification

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View synthesis with 3d object segmentation-based asynchronous blending and boundary misalignment rectification

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VISUAL COMPUTER 2016年第6-8期32卷 989-999页

作者： Liu, Jing Li, Chunpeng Fan, Xuefeng Wang, Zhaoqi Shi, Min Yang, Jie Chinese Acad Sci Beijing Key Lab Mobile Comp & Pervas Device Inst Comp Technol 6 Kexueyuan South Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci 19A Yuquan Rd Beijing 100049 Peoples R China North China Elect Power Univ Sch Control & Comp Engn 2 Beinong Rd Beijing 102206 Peoples R China

Numerous depth image-based rendering algorithms have been proposed to synthesize the virtual view for the free viewpoint television. However, inaccuracies in the depth map cause visual artifacts in the virtual view. In this paper, we propose a novel virtual view synthesis framework to create the virtual view of the scene. Here, we incorporate a trilateral depth filter with local texture information, spatial proximity, and color similarity to remove the ghost contours by rectifying the misalignment between the depth map and its associated color image. To further enhance the quality of the synthesized virtual views, we partition the scene into different 3d object segments based on the color image and depth map. Each 3d object segment is warped and blended independently to avoid mixing the pixels belonging to different parts of the scene. The evaluation results indicate that the proposed method significantly improves the quality of the synthesized virtual view compared with other methods and are qualitatively very similar to the ground truth. In addition, it also performs well in real-world scenes.

关键词： 3d object segmentation Trilateral depth filter Virtual view synthesis Boundary misalignment

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Automatic cables segmentation from a substation device based on 3d point cloud

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MACHINE VISION ANd APPLICATIONS 2023年第1期34卷 1-17页

作者： Yuan, Qianjin Chang, Jing Luo, Yong Ma, Tianlei Wang, dongshu Zhengzhou Univ Sch Elect Engn Zhengzhou Peoples R China

The point cloud segmentation of a substation device attached with cables is the basis of substation identification and reconstruction. However, it is limited by a number of factors including the huge amount of point cloud data of a substation device, irregular shape, unclear feature distinction due to the auxiliary point cloud data attached to the main body of a device. Therefore, the segmentation efficiency of a substation device is very low. In order to improve the accuracy and efficiency of the point cloud segmentation, this paper proposes a method to segment the attached cables point cloud of a substation device by using the shape feature of point cloud. Firstly, according to the spatial position of the point cloud of a substation device, octree is used to conduct voxelization of the point cloud, and the point cloud resampling is operated according to point cloud density of each voxel, so as to reduce original point cloud data and improve computing efficiency. Then Mean Shift algorithm is used to locate the center axis of the point cloud, and cylinder growth method is used to initially segment cables data and locate the end of each cable. Finally, points of the end are used as seed points to carry out a region growth based on shape feature of the point cloud to realize effective segmentation of cables data. In the experiment, 303 sets of point cloud of devices are selected, including circuit breaker, voltage transformer, transformer, etc. The final result shows that the successful segmentation rate of this method reaches 95.34%, which effectively proves the feasibility of this method.

关键词： Point cloud 3d object segmentation Cable segmentation Mean shift Region growth

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Research and application of indoor 3d object detection based on Lidar and Monocular camera 5

Research and application of indoor 3D object detection based...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Jiang, Yi Sun, Bingyu Chongqing Three Gorges Univ Sch Mech Engn Wanzhou 404100 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China

ISBN: (纸本)9798350386783;9798350386776

With the increasing degree of automation of indoor mobile robots, the perception requirements of indoor objects are also higher and higher, which needs to clarify the types of objects and master their three-dimensional size information. In this scheme, the combination of lidar and monocular cameras is adopted to compensate for the deficiency of a single sensor. The data-cascading network structure is adopted. Firstly, the YOLOP model detects and segments the image in two dimensions. After that, the PointNet model performs instance segmentation on the object's point cloud, followed by 3d bounding box regression to identify the target object. The network is used to determine the types of objects and obtain their three-dimensional size information. Through data set and field verification, the proposed data cascade network is superior to most previous methods. It can accomplish the detection task of indoor 3d objects based on lidar and monocular cameras.

关键词： YOLOP PointNet 3d object segmentation Indoor partition

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3d Instance segmentation Using deep Learning on RGB-d Indoor data

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Computers, Materials & Continua 2022年第9期72卷 5777-5791页

作者： Siddiqui Muhammad Yasir Amin Muhammad Sadiq Hyunsik Ahn Department of Robot System Engineering Tongmyong UniversityBusan48520Korea Department of Information&Technology University of Central PunjabLahorePakistan Department of Electronics Engineering Tongmyong UniversityBusan48520Korea

3d object recognition is a challenging task for intelligent and robot systems in industrial and home indoor *** is critical for such systems to recognize and segment the 3d object instances that they encounter on a frequent *** computer vision,graphics,and machine learning fields have all given it a lot of ***,3d segmentation was done with hand-crafted features and designed approaches that didn’t achieve acceptable performance and couldn’t be generalized to large-scale *** learning approaches have lately become the preferred method for 3d segmentation challenges by their great success in 2d computer ***,the task of instance segmentation is currently less *** this paper,we propose a novel approach for efficient 3d instance segmentation using red green blue and depth(RGB-d)data based on deep *** 2d region based convolutional neural networks(Mask R-CNN)deep learning model with point based rending module is adapted to integrate with depth information to recognize and segment 3d instances of *** order to generate 3d point cloud coordinates(x,y,z),segmented 2d pixels(u,v)of recognized object regions in the RGB image are merged into(u,v)points of the depth ***,we conducted an experiment and analysis to compare our proposed method from various points of view and *** experimentation shows the proposed 3d object recognition and instance segmentation are sufficiently beneficial to support object handling in robotic and intelligent systems.

关键词： Instance segmentation 3d object segmentation deep learning point cloud coordinates

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Ant Colony Optimization Inspired Algorithm for 3d object segmentation 1

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12th International Work-Conference on Artificial Neural Networks (IWANN)

作者： Arnay, Rafael Acosta, Leopoldo Univ La Laguna Dept Syst Engn & Control & Comp Architecture San Cristobal la Laguna 38204 Spain

ISBN: (数字)9783642386794

ISBN: (纸本)9783642386787

In this work, an ACO-based approach to the problem of 3d object segmentation is presented. Ant Colony Optimization (ACO) meta-heuristic uses a set of agents to explore a search space, gathering local information and utilizing their common memory to obtain global solutions. In our approach to the 3d segmentation problem, the artificial ants start their exploratory movements in the outer contour of the object. They explore the surface of the object influenced by its curvature and by the trails followed by other agents. After a number of generations, particular solutions of the agents converge to the best global paths, which are used as borders to segment the object's parts. This convergence mechanism avoids over-segmentation, detecting regions based on the global structure of the object and not on local information only.

关键词： Ant Colony Optimization 3d object segmentation

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Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds

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INTERNATIONAL JOURNAL OF APPLIEd EARTH OBSERVATION ANd GEOINFORMATION 2021年 104卷

作者： Luo, Huan Zheng, Quan Fang, Lina Guo, Yingya Guo, Wenzhong Wang, Cheng Li, Jonathan Fuzhou Univ Coll Comp Sci & Big Data Fuzhou 350108 FJ Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou 350108 FJ Peoples R China Fuzhou Univ Acad Digital China Fujian Fuzhou 350108 FJ Peoples R China Xiamen Univ Sch Informat Fujian Key Lab Sensing & Comp Smart Cities Xiamen 361005 FJ Peoples R China Univ Waterloo Dept Geog & Environm Management Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada

due to the advantages of 3d point clouds over 2d optical images, the related researches on scene understanding in 3d point clouds have been increasingly attracting wide attention from academy and industry. However, many 3d scene understanding methods largely require abundant supervised information for training a data-driven model. The acquisition of such supervised information relies on manual annotations which are laborious and arduous. Therefore, to mitigate such manual efforts for annotating training samples, this paper studies a unified neural network to segment 3d objects out of point clouds interactively. Particularly, to improve the segmentation performance on the accurate object segmentation, the boundary information of 3d objects in point clouds are encoded as a boundary energy term in the Markov Random Field (MRF) model. Moreover, the MRF model with the boundary energy term is naturally integrated with the Graphical Neural Network (GNN) to obtain a compact representation for generating the boundary-preserved 3d objects. The proposed method is evaluated on two point clouds datasets obtained from different types of laser scanning systems, i.e. terrestrial laser scanning system and mobile laser scanning system. Comparative experiments show that the proposed method is superior and effective in 3d objects segmentation in different point-cloud scenarios.

关键词： Point Cloud 3d object segmentation Boundary Constraint Graph Neural Network Markov Random Field

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Unsupervised segmentation of unknown objects in complex environments

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AUTONOMOUS ROBOTS 2016年第5期40卷 805-829页

作者： Asif, Umar Bennamoun, Mohammed Sohel, Ferdous Univ Western Australia Sch CSSE Perth WA 6009 Australia Murdoch Univ Sch Engn & Informat Technol Perth WA Australia

This paper presents a novel object segmentation approach for highly complex indoor scenes. Our approach starts with a novel algorithm which partitions the scene into distinct regions whose boundaries accurately conform to the physical object boundaries in the scene. Next, we propose a novel perceptual grouping algorithm based on local cues (e.g., 3d proximity, co-planarity, and shape convexity) to merge these regions into object hypotheses. Our extensive experimental evaluations demonstrate that our object segmentation results are superior compared to the state-of-the-art methods.

关键词： 3d object segmentation object localization object detection

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