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Recurrent Slice Networks for 3D Segmentation of Point Clouds
[article]
2018
arXiv
pre-print
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Point clouds are an efficient data format for 3D data. ...
However, existing 3D segmentation methods for point clouds either do not model local dependencies pointnet or require added computations kd-net,pointnet2. ...
In this paper, we approach 3D semantic segmentation tasks by directly dealing with point clouds. A simple network, a Recurrent Slice Network (RSNet), is designed for 3D segmentation tasks. ...
arXiv:1802.04402v2
fatcat:5xptznlzrfbv5n6obbf7f7qzii
Recurrent Slice Networks for 3D Segmentation of Point Clouds
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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Point clouds are an efficient data format for 3D data. However, existing 3D segmentation methods for point clouds either do not model local dependencies [21] or require added computations [14, 23] . ...
It is a combination of a novel slice pooling layer, Recurrent Neural Network (RNN) layers, and a slice unpooling layer. ...
In this paper, we approach 3D semantic segmentation tasks by directly dealing with point clouds. A simple network, a Recurrent Slice Network (RSNet), is designed for 3D segmentation tasks. ...
doi:10.1109/cvpr.2018.00278
dblp:conf/cvpr/HuangWN18
fatcat:vulz4tcqofewvm4yktvu2pm7wy
Exploiting Structured CNNs For Semantic Segmentation Of Unstructured Point Clouds From LiDAR Sensor
2021
Remote Sensing
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We propose a projection-based scheme that performs an angle-wise slicing of large 3D point clouds and transforms those slices into 2D grids. ...
Accurate semantic segmentation of 3D point clouds is a long-standing problem in remote sensing and computer vision. ...
An example of RNNbased method for segmentation of point cloud is recurrent slice network (RSNet) [71] where unordered and unstructured point clouds are transformed into an ordered sequence of vectors ...
doi:10.3390/rs13183621
fatcat:koo7prv7ojdmtgnl3vvgcsnixu
Deep Learning on Point Clouds and Its Application: A Survey
2019
Sensors
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The applications related to point cloud feature learning, including 3D object classification, semantic segmentation, and 3D object detection, are introduced, and the datasets and evaluation metrics are ...
Recently, many researchers have adapted it into the applications of the point cloud. ...
Acknowledgments: The authors own many thanks to the anonymous reviewers for their instructive comments.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s19194188
fatcat:s64jm5t5xbhzjk7jxgrodehucu
LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices
[article]
2021
arXiv
pre-print
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Here, we propose LatticeNet, a novel approach for 3D semantic segmentation, which takes raw point clouds as input. ...
We present results of 3D segmentation on multiple datasets where our method achieves state-of-the-art performance. We also extend and evaluate our network for instance and dynamic object segmentation. ...
In the recurrent network the input is an ordered set of point clouds P seq and the output are class probabilities for the last point cloud of the sequence. ...
arXiv:2108.03917v1
fatcat:mvmekghirjejbda7sqanwx6jwa
Research for Recognizing Individual Parts of Bridge Using Point Cloud Data with Deep Learning
2020
Journal of Japan Society for Fuzzy Theory and Intelligent Informatics
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Ulrich: "Recurrent Slice Networks
for 3D Segmentation of Point Clouds," Computer Vision and
Pattern Recognition, pp. 2626-2635, 2018.
[7] L. Jonathan, S. Evan, and D. ...
Leonidas: "PointNet:
Deep Learning on Point Sets for 3D Classification and
Segmentation," Computer Vision and Pattern Recognition,
pp. 652-660, 2017.
[6] H. Qiangui, W. Weiyue, and N. ...
In this background, a method was proposed for generating a three-dimensional model of a bridge using the point cloud data obtained from the laser scanner on the ground or UAV. ...
doi:10.3156/jsoft.32.1_627
fatcat:tktkyhyncfdyvetzgfdfeaii6e
Slice-Based Instance and Semantic Segmentation for Low-Channel Roadside LiDAR Data
2020
Remote Sensing
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This paper presents a novel segmentation (slice-based) method for point clouds of roadside LiDAR. ...
The result implies that the slice-based method can obtain a good segmentation effect and the slice has good potential for point cloud segmentation. ...
We are very thankful to the reviewers for their time and efforts. Their comments and suggestions greatly improved the quality of this paper. ...
doi:10.3390/rs12223830
fatcat:j5qelcpjczco7i3s7h3vrnpj2q
Slice-Guided Components Detection and Spatial Semantics Acquisition of Indoor Point Clouds
2022
Sensors
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., the meaningful parts of indoor objects) and obtaining their spatial relationships (e.g., adjacent, in the left of, etc.) is crucial for scene reconstruction and understanding. ...
Specifically, we sliced the indoor scene model into many layers and transformed each slice into a set of two-dimensional (2D) profiles by resampling. ...
Acknowledgments: This study is supported by the Nature Science Foundation of China under Grant No. 61872291References
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s22031121
pmid:35161865
pmcid:PMC8840638
fatcat:gniglrxiorf6boqbxbbrqfig4e
DAR-Net: Dynamic Aggregation Network for Semantic Scene Segmentation
[article]
2019
arXiv
pre-print
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Traditional grid/neighbor-based static pooling has become a constraint for point cloud geometry analysis. ...
In this paper, we propose DAR-Net, a novel network architecture that focuses on dynamic feature aggregation. ...
For context completeness, traditional 3D analysis networks that do not operate on point clouds are first introduced. ...
arXiv:1907.12022v2
fatcat:jywzxhe2sbc3hhntbsft2w645m
Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds
2021
Virtual Archaeology Review
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</p><p>Highlights:</p><ul><li><p>Semantic segmentation of built heritage point clouds through deep neural networks can provide performances comparable to those of more consolidated state-of-the-art ML ...
<p class="VARAbstract">The growing availability of three-dimensional (3D) data, such as point clouds, coming from Light Detection and Ranging (LiDAR), Mobile Mapping Systems (MMSs) or Unmanned Aerial Vehicles ...
Conclusions In this paper, a new approach for the semantic segmentation of heritage point clouds is presented. ...
doi:10.4995/var.2021.15318
fatcat:icaetu2h2rapbcjvttaetv4wu4
Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Our network can be applied to tasks ranging from object classification, part segmentation, to 3D feature matching and label alignment. ...
Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. ...
Acknowledgements This work is supported in part by the National Key R&D Program of China (No. 2017YFA0700800), and the National Natural Science Foundation of China (No. 61772332, ...
doi:10.1609/aaai.v34i07.6965
fatcat:ovpds3q4sfbjxe26iopo45p5am
Advances in Processing, Mining, and Learning Complex Data: From Foundations to Real-World Applications
2018
Complexity
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convolutional recurrent layers for magnetic resonance imaging of prostate image segmentation. ...
However, those approaches mainly paid attention to features and contexts within each single slice of a 3D volume. ...
The editors also wish to thank the anonymous reviewers for their careful reading of the manuscripts submitted to this special issue collection and their many insightful comments and suggestions. ...
doi:10.1155/2018/7861860
fatcat:6mc7cqtqzjcjlghqbmlhb5hifa
SEMANTIC SEGMENTATION OF INDOOR POINT CLOUDS USING CONVOLUTIONAL NEURAL NETWORK
2017
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Therefore we propose to employ deep learning techniques for the semantic segmentation of point clouds into meaningful parts. ...
The method is tested both for a mobile laser scanner point cloud, and a larger scale synthetically generated data. ...
A convolutional neural network is designed for 3D data to obtain semantic segmentation of indoor point clouds. ...
doi:10.5194/isprs-annals-iv-4-w4-101-2017
fatcat:4sopeivzxvgyzczawiols2veki
JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds
[article]
2019
arXiv
pre-print
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In this paper, we propose a novel joint instance and semantic segmentation approach, which is called JSNet, in order to address the instance and semantic segmentation of 3D point clouds simultaneously. ...
Secondly, to obtain more discriminative features, a point cloud feature fusion module is proposed to fuse the different layer features of the backbone network. ...
Acknowledgements This work was supported by the National Natural Science Foundation of China under Grants 61772213 and 91748204. ...
arXiv:1912.09654v1
fatcat:kzetcmbf4ffs5aw5alod4fapou
JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
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The file type is application/pdf.
In this paper, we propose a novel joint instance and semantic segmentation approach, which is called JSNet, in order to address the instance and semantic segmentation of 3D point clouds simultaneously. ...
Secondly, to obtain more discriminative features, a point cloud feature fusion module is proposed to fuse the different layer features of the backbone network. ...
Acknowledgements This work was supported by the National Natural Science Foundation of China under Grants 61772213 and 91748204. ...
doi:10.1609/aaai.v34i07.6994
fatcat:dbjrrbhoibdx3dq6yx34ey66zy
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