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A domain adaptation framework for cross-modality SAR 3d reconstruction point clouds segmentation utilizing LidAR data

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INTERNATIONAL JOURNAL OF APPLIEd EARTH OBSERVATION ANd GEOINFORMATION 2024年 133卷

作者： Wang, Muhan Qiu, Xiaolan Zhang, Zhe Gao, Silin Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China China Natl Key Lab Microwave Imaging Beijing 100190 Peoples R China Key Lab Technol Geospatial Informat Proc & Applica Beijing 100090 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100094 Peoples R China Suzhou Key Lab Microwave Imaging Proc & Applicat T Suzhou 215128 Peoples R China Suzhou Aerosp Informat Res Inst Suzhou 215128 Peoples R China

Synthetic aperture radar (SAR) 3d point clouds reconstruction can eliminate the problems of layover in 2d SAR image projections, the recognition of the reconstructed point cloud can significantly enhance target identification and information extraction. Current SAR 3d point cloud segmentation methods, based on traditional machine learning clustering approaches, suffer from poor automation and accuracy. However, the absence of publicly labeled SAR datasets impedes the progress of deep learning-based SAR 3d point cloud segmentation methods. To tackle the aforementioned challenges, we introduce, for the first time, an alternative training approach for SAR 3d point cloud segmentation using LidAR annotated data, which offers a more abundant sample pool, thus alleviating the lack of training sets. Nevertheless, a segmentation model trained on LidAR point clouds exhibits a significant decline in performance when directly applied to SAR 3d reconstructed point clouds due to the cross-domain discrepancy. This research presents a pioneering domain adaptation 3d semantic segmentation framework to implement cross-modal learning for SAR point clouds. In our scheme, a simple yet effective technique is developed to achieve segmentation of SAR double-bounce scattering regions called SARdBS-Mix, which employs a mixing strategy, to capture the distinctive reflection characteristics of SAR data. Furthermore, we implement approaches including center alignment and normalization, local augmentation, and weighted cross entropy to mitigate LidAR and SAR domain gap and class imbalances. The experimental results validate the feasibility and effectiveness of the proposed method for SAR 3d reconstructed point cloud segmentation.

关键词： Synthetic aperture radar (SAR) Point cloud 3d semantic segmentation domain adaptation Cross-modal learning

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ECLAIR: A High-Fidelity Aerial LidAR dataset for semantic segmentation

ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Se...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Melekhov, Iaroslav Umashankar, Anand Kim, Hyeong-Jin Serkov, Vladislav Argyle, dusty Sharper Shape Salt Lake City UT 84115 USA Aalto Univ Espoo Finland

ISBN: (纸本)9798350365474

We introduce ECLAIR (Extended Classification of Lidar for AI Recognition), a new outdoor large-scale aerial LidAR dataset designed specifically for advancing research in point cloud semantic segmentation. As the most extensive and diverse collection of its kind to date, the dataset covers a total area of 10km(2) with close to 600 million points and features eleven distinct object categories. To guarantee the dataset's quality and utility, we have thoroughly curated the point labels through an internal team of experts, ensuring accuracy and consistency in semantic labeling. The dataset is engineered to move forward the fields of 3d urban modeling, scene understanding, and utility infrastructure management by presenting new challenges and potential applications. As a benchmark, we report qualitative and quantitative analysis of a voxel-based point cloud segmentation approach based on the Minkowski Engine. We release the dataset as open-source and it can be accessed at https:// ***/SharperShape/eclairdataset

关键词： 3d semantic segmentation deep learning Lidar dataset minkowski engine pointcloud data

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dual-Neighborhood Feature Aggregation Network for Point Cloud semantic segmentation 34

Dual-Neighborhood Feature Aggregation Network for Point Clou...

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34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Chen, Minghong Zhang, Guanghui Shi, Wenjun Zhu, dongchen Zhang, Xiaolin Li, Jiamao Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol State Key Lab Transducer Technol Bion Vis Syst Lab Shanghai 200050 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China ShanghaiTech Univ Sch Informat & Technol Shanghai 200050 Peoples R China Xiongan Inst Innovat Xiongan 071700 Peoples R China Univ Sci & Technol China Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9798350397444

Neighborhood construction plays a key role in point cloud processing. However, existing models only use a single neighborhood construction method to extract neighborhood features, which limits their scene understanding ability. In this paper, we propose a learnable dual-Neighborhood Feature Aggregation (dNFA) module embedded in the encoder that builds and aggregates comprehensive surrounding knowledge of point clouds. In this module, we first construct two kinds of neighborhoods and design corresponding feature enhancement blocks, including a Basic Local Structure Encoding (BLSE) block and an Extended Context Encoding (ECE) block. The two blocks mine structural and contextual cues for enhancing neighborhood features, respectively. Second, we propose a Geometry-Aware Compound Aggregation (GACA) block, which introduces a functionally complementary compound pooling strategy to aggregate richer neighborhood features. To fully learn the neighborhood distribution, we absorb the geometric location information during the aggregation process. The proposed module is integrated into an MLP-based large-scale 3d processing architecture, which constitutes a 3d semantic segmentation network called dNFA-Net. Extensive experiments on public datasets containing indoor and outdoor scenes validate the superiority of dNFA-Net.

关键词： 3d semantic segmentation dual neighborhood construction feature enhancement compound pooling

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Clustering-Based Refinement for 3d Human Body Parts segmentation 17th

Clustering-Based Refinement for 3D Human Body Parts Segmenta...

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17th International Conference on Intelligent Autonomous Systems (IAS)

作者： Barcellona, Leonardo Terreran, Matteo Evangelista, daniele Ghidoni, Stefano Univ Padua Dept Informat Engn DEI Padua Italy

ISBN: (纸本)9783031222153;9783031222160

A common approach to address human body parts segmentation on 3d data involves the use of a 2d segmentation network and 3d projection. Following this approach, several errors could be introduced in the final 3d segmentation output, such as segmentation errors and reprojection errors. Such errors are even more significant when considering very small body parts such as hands. In this paper, we propose a new algorithm that aims to reduce such errors and improve 3d segmentation of human body parts. The algorithm detects noise points and wrong clusters using dBSCAN algorithm, and changes the labels of the points exploiting the shape and position of the clusters. We evaluated the proposed algorithm on the 3dPeople synthetic dataset and on a real dataset, highlighting how it can greatly improve the 3d segmentation of small body parts like hands. With our algorithm we achieved an improvement up to 4.68% of IoU on the synthetic dataset and up to 2.30% of IoU in the real scenario.

关键词： Human parsing 3d semantic segmentation 3d clustering

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3d Shape segmentation with Geometric deep Learning 20th

3D Shape Segmentation with Geometric Deep Learning

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20th International Conference on Image Analysis and Processing (ICIAP)

作者： Boscaini, davide Poiesi, Fabio Fdn Bruno Kessler Technol Vis Via Sommarive 18 I-38123 Trento Italy

ISBN: (纸本)9783030306427;9783030306410

The semantic segmentation of 3d shapes with a high-density of vertices could be impractical due to large memory requirements. To make this problem computationally tractable, we propose a neural-network based approach that produces 3d augmented views of the 3d shape to solve the whole segmentation as sub-segmentation problems. 3d augmented views are obtained by projecting vertices and normals of a 3d shape onto 2d regular grids taken from different viewpoints around the shape. These 3d views are then processed by a Convolutional Neural Network to produce a probability distribution function (pdf) over the set of the semantic classes for each vertex. These pdfs are then re-projected on the original 3d shape and postprocessed using contextual information through Conditional Random Fields. We validate our approach using 3d shapes of publicly available datasets and of real objects that are reconstructed using photogrammetry techniques. We compare our approach against state-of-the-art alternatives.

关键词： 3d semantic segmentation Geometric deep learning

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Automatic segmentation of the Prostate on 3d CT Images by Using Multiple deep Learning Networks 18

Automatic Segmentation of the Prostate on 3D CT Images by Us...

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5th International Conference on Biomedical and Bioinformatics Engineering (ICBBE)

作者： Xiong, Jiayang Jiang, Luan Li, Qiang Shanghai Jiao Tong Univ 800 Dongchuan Shanghai Peoples R China Shanghai United Imaging Healthcare Co Ltd 2258 Chengbei Rd Shanghai 201815 Peoples R China

ISBN: (纸本)9781450365611

Automatic segmentation of the prostate on CT images has many applications in prostate cancer diagnosis and therapy. However, prostate segmentation from CT images is a very challenging task due to the low contrast of soft tissue and the large variations of the prostate shape. In this paper, we propose an automatic segmentation method by using a two dimension (2-d) and a three dimension (3-d) convolutional neural network (CNN). First, instead of segmenting the whole image, we extract the volumes of interest (VOI) accurately to remove irrelevant regions by using a CNN based VOI extraction method (CBVEM). Then, we use the 3-d CNN to learn the holistic three-dimension deep features for distinguishing the prostate voxels from the non-prostate voxels in order to obtain the segmentation results. deep learning networks can automatically learn the deep features based on the data, which are different from the handcrafted features. The proposed method has been evaluated on a dataset of 150 prostate CT images. Experimental results show that our method achieved a dice similarity coefficient of 89.74% compared to the manual segmentation. Our deep learning based method is faster and returns similar results compared to those atlas-based, deformable model-based and feature-based classification methods. due to the CBVEM and 3-d CNN, our method also achieves better performances on the same data in a minimum processing time compared to other deep CNN based methods. This proposed method for automatic segmentation of the prostate on 3-d CT images can have a variety of clinical applications.

关键词： 3d semantic segmentation CT images deep learning prostate

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PointResNet: A grape bunches point cloud semantic segmentation model based on feature enhancement and improved PointNet plus

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COMPUTERS ANd ELECTRONICS IN AGRICULTURE 2024年 224卷

作者： Luo, Jiangtao Zhang, dongbo Luo, Lufeng Yi, Tao Xiangtan Univ Sch Automat & Elect Informat Xiangtan 411105 Peoples R China Foshan Univ Sch Mechatron Engn & Automat Foshan 528000 Peoples R China

As a type of representative bunch-type fruit, the collision-free and undamaged harvesting of grapes is of great significance. To obtain accurate 3d spatial semantic information, this paper proposes a method for multifeature enhanced semantic segmentation model based on Mask R-CNN and PointResNet(improved PointNet++). Firstly, a depth camera is used to obtain RGBd images. The RGB images are then inputted into the Mask-RCNN network for fast detection of grape bunches. The color and depth information are fused and transformed into point cloud data, followed by the estimation of normal vectors. Finally, the nine-dimensional point cloud, which include spatial location, color information, and surface structure information, are inputted into the PointResNet network to achieve semantic segmentation of grape bunches, peduncles, and leaves. This process obtains the extraction of spatial semantic information from the surrounding area of the bunches. The experimental results show that by incorporating normal vector and color features, the overall accuracy of point cloud segmentation increases to 96.5%, with a mean accuracy of 90.3%. This represents a significant improvement of 7.9% and 16.6% compared to using only positional features. The results demonstrate that the model method presented in this paper can effectively provide precise 3d semantic information to the robot while ensuring both speed and accuracy. This lays the groundwork for subsequent collision-free and undamaged picking.

关键词： Feature enhancement 3d semantic segmentation Grape segmentation Point cloud

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Boosting few-shot point cloud segmentation with intra-class correlation and iterative prototype fusion

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Computer Vision and Image Understanding 2025年 258卷

作者： Zhang, Xindan Li, Ying Zhang, Xinnian College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China of Information and Communication Technology Ajou University South Korea

semantic segmentation of 3d point clouds is often limited by the challenge of obtaining labeled data. Few-shot point cloud segmentation methods, which can learn previously unseen categories, help reduce reliance on labeled datasets. However, existing methods are susceptible to correlation noise and suffer from significant discrepancies between support prototypes and query features. To address these issues, we first introduce an intra-class correlation enhancement module for filtering correlation noise driven by inter-class similarity and intra-class diversity. Second, to better represent the target classes, we propose an iterative prototype fusion module that adapts the query point cloud feature space, mitigating the problem of object variations in the support set and query set. Extensive experiments on S3dIS and ScanNet benchmark datasets demonstrate that our approach achieves competitive performance with state-of-the-art methods. © 2025 Elsevier Inc.

关键词： 3d semantic segmentation Few-shot learning

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NeiEA-NET: semantic segmentation of large-scale point cloud scene via neighbor enhancement and aggregation

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INTERNATIONAL JOURNAL OF APPLIEd EARTH OBSERVATION ANd GEOINFORMATION 2023年 119卷

作者： Xu, Yongyang Tang, Wei Zeng, Ziyin Wu, Weichao Wan, Jie Guo, Han Xie, Zhong China Univ Geosci Sch Comp Sci Wuhan Peoples R China State Key Lab Geoinformat Engn Xian 710054 Peoples R China Guangdong Hong Kong Macau Joint Lab Smart Cities Hong Kong 518000 Guangdong Peoples R China China Univ Geosci Key Lab geol & evaluat Minist Educ Wuhan Peoples R China Minist Nat Resources Key Lab Urban Land Resources Monitoring & Simulat Shenzhen 518034 Peoples R China

3d point cloud semantic segmentation is crucial for 3d environment perception and scene understanding, where learning of local context in point clouds is a crucial challenge. Existing approaches typically explore local context based on the predefined neighbors of point clouds. However, the widely used K-nearest neighbor algorithm (KNN) is far from optimal in defining local neighbors. In this study, we propose NeiEA-Net, a conceptually simple and effective network for point cloud semantic segmentation. The key to our approach is to optimize the local neighbors in 3d Euclidean space by taking full advantage of high-dimensional feature space as much as possible. In addition, we introduce a neighbor feature aggregation module to adaptively aggregate features with different scales in the local neighbors to further reduce the redundant information, thereby effectively learning the local details of point clouds. Experiments conducted on three large-scale benchmarks, S3dIS, Toronto3d and SensatUrban, demonstrate the superiority of our network.

关键词： Point cloud 3d semantic segmentation Large-scale scene KNN

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LEARd-Net: semantic segmentation for large-scale point cloud scene

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INTERNATIONAL JOURNAL OF APPLIEd EARTH OBSERVATION ANd GEOINFORMATION 2022年 112卷

作者： Zeng, Ziyin Xu, Yongyang Xie, Zhong Tang, Wei Wan, Jie Wu, Weichao China Univ Geosci Sch Comp Sci Wuhan Peoples R China China Univ Geosci Key Lab Geol & Evaluat Minist Educ Wuhan Peoples R China

Given the prominence of 3d sensors in recent years, 3d point cloud scene data are worthy to be further investigated. Point cloud scene understanding is a challenging task because of its characteristics of large-scale and discrete. In this study, we propose a network called LEARd-Net, focuses on semantic segmentation for the large-scale point cloud scene data with color information. The proposed network contains three main components: (1) To fully utilize color information of point clouds rather than just as initial input features, we propose a robust local feature extraction module (LFE) to benefit the network focus on both spatial geometric structure, color information and semantic features. (2) We propose a local feature aggregation module (LFA) to benefit the network to focus on the local significant features while also focus on the entire local neighbor. (3) To allow the network to focus on both local and comprehensive features, we use residual and dense connections (Residense) to connect different-level LFE and LFA modules. Comparing with state-of-the-art networks on several large-scale benchmark datasets, including S3dIS, Toronto3d and semantic3d, we demonstrate the effectiveness of our LEARd-Net.

关键词： 3d point cloud 3d semantic segmentation Feature encoding Skip connection

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