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Point Attention Network for Semantic Segmentation of 3D Point Clouds
[article]
2019
arXiv
pre-print
Preserved Fulltext
We propose a point attention network that learns rich local shape features and their contextual correlations for 3D point cloud semantic segmentation. ...
predicting dense labels for 3D point cloud segmentation. ...
Local Attention-Edge Convolution (LAE-Conv) The Local Attention-Edge Convolution (LAE-Conv) layer forms the basic component of our point attention network architecture for 3D point cloud semantic segmentation ...
arXiv:1909.12663v1
fatcat:vpk2ae4m7zfqppy2c43o2fxd5i
AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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3D point cloud semantic segmentation has attracted wide attention with its extensive applications in autonomous driving, AR/VR, and robot sensing fields. ...
Adversarial learning, which explores the underlying relationship between labels in high-dimensional space, opens up a new way in 3D point cloud semantic segmentation. ...
In this paper, we design a framework Attention Adversarial Networks (AttAN) for 3D point cloud semantic segmentation. ...
doi:10.24963/ijcai.2020/110
dblp:conf/ijcai/ZhangMJLS20
fatcat:for7cesvqrb7dass4ecer3vejy
PyramNet: Point Cloud Pyramid Attention Network and Graph Embedding Module for Classification and Segmentation
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1906.03299v1 fatcat:yl4fciwourhgxjln2d2lfdit7q
In this paper, we propose a simple and effective network, which is named PyramNet, suites for point cloud object classification and semantic segmentation in 3D scene. ...
With the tide of artificial intelligence, we try to apply deep learning to understand 3D data. Point cloud is an important 3D data structure, which can accurately and directly reflect the real world. ...
The Pyramid Attention Network (PAN) combines features of different resolutions and different semantic strengths, especially for semantic segmentation tasks in 3D scene. ...
arXiv:1906.03299v2
fatcat:cw2t5czmdzhhvmdgjfybsh5yvy
FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection
[article]
2021
arXiv
pre-print
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First, semantic information is obtained for 2D images and 3D Lidar point clouds based on 2D and 3D segmentation approaches. ...
Finally, the point clouds painted with the fused semantic label are sent to the 3D detector for obtaining the 3D objection results. ...
This approach employees the 2D image semantic segmentation results from an offthe-shelf neural network first and then adds them into a point-cloud-based 3D object detection based on the 2D-3D projection ...
arXiv:2106.12449v2
fatcat:rboxbzoa3zfipifw4ff4qf2ofq
Learning 3D Semantics from Pose-Noisy 2D Images with Hierarchical Full Attention Network
[article]
2022
arXiv
pre-print
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A hierarchical full attention network~(HiFANet) is designed to sequentially aggregates patch, bag-of-frames and inter-point semantic cues, with hierarchical attention mechanism tailored for different level ...
We propose a novel framework to learn 3D point cloud semantics from 2D multi-view image observations containing pose error. ...
This paper utilizes features from multi-view patches sampled from camera images, which are not accurately aligned with 3D point cloud, to benefit the semantic segmentation of 3D point cloud. ...
arXiv:2204.08084v3
fatcat:q3xxfmtqxrekbgvkgirm7iva6u
Semantic Point Cloud Segmentation Using Fast Deep Neural Network and DCRF
2021
Sensors
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This paper presents a fast deep neural network model with Dense Conditional Random Field (DCRF) as a post-processing method, which can perform accurate semantic segmentation for 3D point cloud scene. ...
On this basis, a compact but flexible framework is introduced for performing segmentation to the semantics of point clouds concurrently, contribute to more precise segmentation. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21082731
pmid:33924465
fatcat:ic3fpjndo5c6bhvoiobyyrajpu
DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Clouds
[article]
2021
arXiv
pre-print
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The DLA module could be easily embedded into various network architectures for point cloud segmentation, naturally resulting in a new 3D semantic segmentation network with an encoder-decoder architecture ...
As there is a lack of 3D point clouds datasets related to the fine-grained building facade, we construct the first large-scale building facade point clouds benchmark dataset for semantic segmentation. ...
So, in this paper, we try to apply the self-attention network to 3D point clouds of building facades for semantic segmentation. ...
arXiv:2106.00376v1
fatcat:2uxepdxeh5fe7nmelhyb2bvrsy
Anchor-Based Spatio-Temporal Attention 3D Convolutional Networks for Dynamic 3D Point Cloud Sequences
[article]
2021
arXiv
pre-print
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and semantic segmentation tasks. ...
Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition ...
Semantic Segmentation Our network for semantic segmentation is tested on the Synthia dataset [21] . ...
arXiv:2012.10860v2
fatcat:3fytldbzw5h25kh5tol6uda3c4
3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation
[article]
2019
arXiv
pre-print
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This paper introduces a novel approach for 3D semantic instance segmentation on point clouds. ...
To get more consistent embeddings for each 3D instance, attention-based k nearest neighbour (KNN) is proposed to assign different weights for different neighbours. ...
The main spotlight of our graph convolutional network is that it uses the attention-based KNN as the aggregator. This is a natural and meaningful operation for point clouds. ...
arXiv:1902.05247v1
fatcat:dxvs6kqgnfey3gt4g7oducdgbq
Point Transformer
[article]
2021
arXiv
pre-print
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Inspired by this success, we investigate the application of self-attention networks to 3D point cloud processing. ...
We design self-attention layers for point clouds and use these to construct self-attention networks for tasks such as semantic scene segmentation, object part segmentation, and object classification. ...
We flesh out this intuition and develop a self-attention layer for 3D point cloud processing. Based on this layer, we construct Point Transformer networks for a variety of 3D understanding tasks. ...
arXiv:2012.09164v2
fatcat:psobzrptavbc7khnrfrkd36zte
Local and global encoder network for semantic segmentation of Airborne laser scanning point clouds
2021
ISPRS journal of photogrammetry and remote sensing (Print)
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In this paper, we present a local and global encoder network (LGENet) for semantic segmentation of ALS point clouds. ...
A B S T R A C T Interpretation of Airborne Laser Scanning (ALS) point clouds is a critical procedure for producing various geoinformation products like 3D city models, digital terrain models and land use ...
Exploiting global contextual information is also researched in 3D deep neural networks for semantic segmentation of point clouds. ...
doi:10.1016/j.isprsjprs.2021.04.016
fatcat:5symndwktrh3zgbzmmzjj7lmqu
PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants
2022
Plant Phenomics
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Then, a deep learning network—PSegNet, was specially designed for segmenting point clouds of several species of plants. ...
In phenotyping, segmentation of 3D point clouds of plant organs such as leaves and stems contributes to automatic growth monitoring and reflects the extent of stress received by the plant. ...
Moreover, the majority of modern neural networks for 3D learning only accept standardized input point clouds, i.e., a fixed number of points for all point clouds. ...
doi:10.34133/2022/9787643
fatcat:xpt253bzanhgppn3lgfkfqtdrm
Global Context Reasoning for Semantic Segmentation of 3D Point Clouds
2020
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
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Global contextual dependency is important for semantic segmentation of 3D point clouds. ...
Experimental results show that our PointGCR module efficiently captures global contextual dependencies and significantly improve the segmentation performance of several existing networks. ...
Point Cloud Segmentation Learning discriminative feature representations from point clouds is the foundation for 3D semantic segmentation. ...
doi:10.1109/wacv45572.2020.9093411
dblp:conf/wacv/MaGLLW20
fatcat:jb5sjimndzegnmqpwkqiuew4ta
PnP-3D: A Plug-and-Play for 3D Point Clouds
[article]
2021
arXiv
pre-print
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With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis. ...
However, there is great potential for development of these networks since the given information of point cloud data has not been fully exploited. ...
Guibas, “Pointnet++: Deep attention network for semantic segmentation of 3d point clouds,”
hierarchical feature learning on point sets in a metric space,” in Pattern Recognition ...
arXiv:2108.07378v2
fatcat:atpotz75ujg3rbcgm3qhkd72ay
Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds
[article]
2020
arXiv
pre-print
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To the best of our knowledge, this is the first method that uses cloud-level weak labels on raw 3D space to train a point cloud semantic segmentation network. ...
Point clouds provide intrinsic geometric information and surface context for scene understanding. Existing methods for point cloud segmentation require a large amount of fully labeled data. ...
Acknowledgement This work was conducted within the Delta-NTU Corporate Lab for Cyber-Physical Systems with funding support from Delta Electronics Inc. and the National Research Foundation (NRF) Singapore ...
arXiv:2003.13035v1
fatcat:tfgqnu7ndzg3lp7kbcj6oqifoy
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