A Panoramic Segmentation Network for Point Cloud

Jin, Yuanyun; Luo, Xiangfeng; Yang, Wenbin; Xie, Shaorong; Liu, Tong

doi:10.1088/1755-1315/440/3/032016

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…This methodology diverges from conventional 3D instance segmentation methods that rely on voting and grouping of 3D orthocoordinates based on a clustering paradigm, which proves to be less effective for large and irregularly shaped objects. [21] [22] [23]. By contrast, our approach does not depend on any handtuned, distance-based clustering.…”

Section: Toward 3d Instance Segmentation Using Yolov4mentioning

confidence: 99%

Efficient 3D Instance Segmentation for Archaeological Sites Using 2D Object Detection and Tracking

kamal Al-anni,

DRAP

2024

IJCDS

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This paper introduces an efficient method for 3D instance segmentation based on 2D object detection, applied to the photogrammetric survey images of archaeological sites. The method capitalizes on the relationship between the 3D model and the set of 2D images utilized to compute it. 2D detections on the images are projected and transformed into a 3D instance segmentation, thus identifying unique objects within the scene. The primary contribution of this work is the development of a semi-automatic image annotation method, augmented by an object tracking technique that leverages the temporal continuity of image sequences. Additionally, a novel ad-hoc evaluation process has been integrated into the conventional annotation-training-testing cycle to determine the necessity of additional annotations. This process tests the consistency of the 3D objects yielded by the 2D detection. The efficacy of the proposed method has been validated on the underwater site of Xlendi in Malta, resulting in complete and accurate 3D instance segmentation. Compared to traditional methods, the object tracking approach adopted has facilitated a 90% reduction in the need for manual annotations, The approach streamlines precise 3D detection, establishing a robust foundation for comprehensive 3D instance segmentation. This enhancement enriches the 3D survey, providing profound insights and facilitating seamless exploration of the Xlendi site from an archaeological perspective.

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Section: Toward 3d Instance Segmentation Using Yolov4mentioning

confidence: 99%

Efficient 3D Instance Segmentation for Archaeological Sites Using 2D Object Detection and Tracking

kamal Al-anni,

DRAP

2024

IJCDS

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“…PointNet++ 18 partitions point clouds into overlapped spheres and extracts local features with PointNet. Except PointNet++, some other works [19][20][21][22][23][24] also consider the local features extraction. Nevertheless, they ignore geometric relations in point clouds.…”

Section: Related Workmentioning

confidence: 99%

“…Pointnet 17 pioneers the point-based methods, which encode each point independently with a shared multi-layer perceptron (MLP) and get global point clouds features for 3D object classification. Pointnet++ 18 and other point-based methods [19][20][21][22][23][24] further consider the local features extraction, which is beneficial to classification. Moreover, the point-based methods can also be used for segmentation task.…”

mentioning

confidence: 99%

Geometric relation based point clouds classification and segmentation

Yang

Sheng

Luo

et al. 2022

Concurrency and Computation

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The inherent disorder and irregularity of 3D point clouds pose great challenges to classification and segmentation tasks. To tackle these problems, we propose a geometric relation based point clouds classification and segmentation network. Specifically, we design two novel modules named geometric relation based convolution (GRC) and relational attention interpolation (RAI) to infer the local relations of point clouds. In GRC module, the convolutional weights and local features are both reasoned from predefined local geometric relations between the central point of each local point clouds and its neighboring points. The global shape awareness is obtained by stacking convolutional layers of the GRC. In RAI, a relational attention interpolation approach is proposed for the segmentation task. The attentional weights of different neighboring points are inferred from local relations of geometry and features, which is capable of guiding RAI to pay more attention to the relevant points and be sensitive to the boundaries of segments. Experimental results show that the proposed method makes full use of the geometric relations between local points, and presents good performance on both classification and segmentation tasks.

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“…Sequential datasets with point-wise annotation, e.g., SemanticKITTI [8], have appeared in recent years. The datasets with both point-wise and instance labels spawn research on 3D semantic segmentation [52] and panoramic segmentation [53].…”

Section: A 3d Lidar Datasetsmentioning

confidence: 99%

Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study

Gao¹,

Pan²,

Li³

et al. 2020

Preprint

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3D LiDAR semantic segmentation is a pivotal task that is widely involved in many applications, such as autonomous driving and robotics. Studies of 3D LiDAR semantic segmentation have recently achieved considerable development, especially in terms of deep learning strategies. However, these studies usually rely heavily on considerable fine annotated data, while point-wise 3D LiDAR datasets are extremely insufficient and expensive to label. The performance limitation caused by the lack of training data is called the data hungry effect. This survey aims to explore whether and how we are hungry for 3D LiDAR data for semantic segmentation. Thus, we first provide an organized review of existing 3D datasets and 3D semantic segmentation methods. Then, we provide an in-depth analysis of three representative datasets and several experiments to evaluate the data hungry effects in different aspects. Efforts to solve data hungry problems are summarized for both 3D LiDAR-focused methods and general-purpose methods. Finally, insightful topics are discussed for future research on data hungry problems and open questions.

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A Panoramic Segmentation Network for Point Cloud

Cited by 3 publications

References 2 publications

Efficient 3D Instance Segmentation for Archaeological Sites Using 2D Object Detection and Tracking

Efficient 3D Instance Segmentation for Archaeological Sites Using 2D Object Detection and Tracking

Geometric relation based point clouds classification and segmentation

Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study

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