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3rd International Conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Bi, Yuxuan Liu, Peng Shi, Jialin Zhang, Tianyi Changchun University of Science and Technology School of Electronic Information Engineering Changchun China

ISBN: (纸本)9798350316674

In order to address the issue of low accuracy in existing multi-modal fusion 3d semantic segmentation models, this study proposes an improved multimodal fusion 3d semantic segmentation model based on LidAR and camera. The model designs a Virtual Point Generation algorithm based on Multi-View Projection, which generates virtual points with depth and semantic information in 3d space by utilizing 2d image space, thereby enhancing the model's capability of extracting high-level features. Additionally, the model extends its receptive field to the 2d image space during the virtual point generation process, addressing the problem of virtual point noise that is difficult to eliminate in 3d space. Compared to other mainstream 3d semantic segmentation models, the proposed segmentation model in this paper achieves an accuracy improvement of 1.29% and 0.18% on the Waymo and nuScenes datasets, respectively. Experimental results demonstrate a significant enhancement in accuracy for the multimodal fusion 3d semantic segmentation model proposed in this study. © 2023 IEEE.

关键词： 3d semantic segmentation multi-modal fusion Multi-View Projection

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PIIE-dSA-Net for 3d semantic segmentation of Urban Indoor and Outdoor datasets

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REMOTE SENSING 2022年第15期14卷 3583页

作者： Gao, Fengjiao Yan, Yiming Lin, Hemin Shi, Ruiyao Heilongjiang Acad Sci Intelligent Mfg Res Inst Harbin 150001 Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China SOPHGO Beijing 100080 Peoples R China

In this paper, a 3d semantic segmentation method is proposed, in which a novel feature extraction framework is introduced assembling point initial information embedding (PIIE) and dynamic self-attention (dSA)-named PIIE-dSA-net. Ideal segmentation accuracy is a challenging task, since the sparse, irregular and disordered structure of point cloud. Currently, taking into account both low-level features and deep features of the point cloud is the more reliable and widely used feature extraction method. Since the asymmetry between the length of the low-level features and deep features, most methods cannot reliably extract and fuse the features as expected and obtain ideal segmentation results. Our PIIE-dSA-net first introduced the PIIE module to maintain the low-level initial point-cloud position and RGB information (optional), and we combined them with deep features extracted by the PAConv backbone. Secondly, we proposed a dSA module by using a learnable weight transformation tensor to transform the combined PIIE features and following a self-attention structure. In this way, we obtain optimized fused low-level and deep features, which is more efficient for segmentation. Experiments show that our PIIE-dSA-net is ranked at least in the top seventh among the most recent published state-of-art methods on the indoor dataset and also made a great improvement than original PAConv on outdoor datasets.

关键词： 3d semantic segmentation point cloud feature extraction self-attention

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From CAd Models to Soft Point Cloud Labels: An Automatic Annotation Pipeline for Cheaply Supervised 3d semantic segmentation

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REMOTE SENSING 2023年第14期15卷 3578-3578页

作者： Humblot-Renaux, Galadrielle Jensen, Simon Buus Mogelmose, Andreas Aalborg Univ Visual Anal & Percept Lab DK-9000 Aalborg Denmark

We propose a fully automatic annotation scheme that takes a raw 3d point cloud with a set of fitted CAd models as input and outputs convincing point-wise labels that can be used as cheap training data for point cloud segmentation. Compared with manual annotations, we show that our automatic labels are accurate while drastically reducing the annotation time and eliminating the need for manual intervention or dataset-specific parameters. Our labeling pipeline outputs semantic classes and soft point-wise object scores, which can either be binarized into standard one-hot-encoded labels, thresholded into weak labels with ambiguous points left unlabeled, or used directly as soft labels during training. We evaluate the label quality and segmentation performance of PointNet++ on a dataset of real industrial point clouds and Scan2CAd, a public dataset of indoor scenes. Our results indicate that reducing supervision in areas that are more difficult to label automatically is beneficial compared with the conventional approach of naively assigning a hard "best guess" label to every point.

关键词： 3d semantic segmentation automatic labeling soft labels point clouds deep learning

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SemRegionNet: Region ensemble 3d semantic instance segmentation network with semantic spatial aware discriminative loss

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NEUROCOMPUTING 2022年 513卷 247-260页

作者： Zhang, Guanghui Zhu, dongchen Shi, Wenjun Li, Jiamao Zhang, Xiaolin Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Bion Vis Syst Lab State Key Lab Transducer Technol 865 Changning Rd Shanghai 200050 Peoples R China Univ Sci & Technol China 96 JinZhai Rd Hefei 230027 Anhui Peoples R China Xiongan Inst Innovat Xiongan 071700 Peoples R China ShanghaiTech Univ 393 Middle Huaxia Rd Shanghai 201210 Peoples R China

The semantic instance segmentation task on 3d data has made great progress. However, for unstructured 3d point cloud data, the mining of regional knowledge and explicit assistance of semantic for the instance segmentation task are still rarely explored. In this paper, we propose a region ensemble structure including random sampling-based set abstraction (RS-SA), an adaptive regional feature complementary (ARFC) module, and an affinity-based regional relational reasoning (AR(3)) module in feature encoding to enhance the point cloud comprehension. Random sampling is introduced into the set abstraction of feature encoding to improve computational efficiency. The ARFC module aims to complement low-level features to adaptively compensate for the information loss caused by random sampling and inherent nonuniformity of point clouds, and the AR(3) module emphasizes mining the potential reasoning relationships among high-level features based on affinity. Furthermore, a novel semantic spatial aware discriminative loss is proposed to improve the discrimination of instance embedding. The proposed region ensemble structure and semantic spatial awareness for discriminative loss are demonstrated promising boosts on the 3d point cloud semantic instance segmentation task, and the framework achieves state-of-the-art performance on the S3dIS, ScanNet-v2, and ShapeNet datasets. (C) 2022 Elsevier B.V. All rights reserved.

关键词： 3d semantic segmentation 3d instance segmentation Region ensemble semantic aware Spatial aware

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A Real-Time Online Learning Framework for Joint 3d Reconstruction and semantic segmentation of Indoor Scenes

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IEEE ROBOTICS ANd AUTOMATION LETTERS 2022年第2期7卷 1332-1339页

作者： Menini, davide Kumar, Suryansh Oswald, Martin R. Sandstrom, Erik Sminchisescu, Cristian Van Gool, Luc Swiss Fed Inst Technol CH-8092 Zurich Switzerland Swiss Fed Inst Technol CVG Grp CH-8092 Zurich Switzerland Google Res Zurich CH-8092 Zurich Switzerland Swiss Fed Inst Technol CVL Zurich Switzerland PSI Villigen Switzerland Katholieke Univ Leuven Leuven Belgium

This letter presents a real-time online vision framework to jointly recover an indoor scene's 3d structure and semantic label. Given noisy depth maps, a camera trajectory, and 2d semantic labels at train time, the proposed deep neural network based approach learns to fuse the depth over frames with suitable semantic labels in the scene space. Our approach exploits the joint volumetric representation of the depth and semantics in the scene feature space to solve this task. For a compelling online fusion of the semantic labels and geometry in real-time, we introduce an efficient vortex pooling block while dropping the use of routing network in online depth fusion to preserve high-frequency surface details. We show that the context information provided by the semantics of the scene helps the depth fusion network learn noise-resistant features. Not only that, it helps overcome the shortcomings of the current online depth fusion method in dealing with thin object structures, thickening artifacts, and false surfaces. Experimental evaluation on the Replica dataset shows that our approach can perform depth fusion at 37 and 10 frames per second with an average reconstruction F-score of 88% and 91%, respectively, depending on the depth map resolution. Moreover, ourmodel shows an average IoU score of 0.515 on the ScanNet 3d semantic benchmark leaderboard. Code and example dataset information is available at https://***/suryanshkumar/ online-joint-depthfusion-and-semantic.

关键词： Online dense 3d reconstruction 3d semantic segmentation deep-learning and robot vision

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Learning Virtual View Selection for 3d Scene semantic segmentation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 4159-4172页

作者： Mu, Tai-Jiang Shen, Ming-Yuan Lai, Yu-Kun Hu, Shi-Min Tsinghua Univ Minist Educ Key Lab Pervas Comp Beijing 100084 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff CF24 4AG Wales

2d-3d joint learning is essential and effective for fundamental 3d vision tasks, such as 3d semantic segmentation, due to the complementary information these two visual modalities contain. Most current 3d scene semantic segmentation methods process 2d images "as they are", i.e., only real captured 2d images are used. However, such captured 2d images may be redundant, with abundant occlusion and/or limited field of view (FoV), leading to poor performance for the current methods involving 2d inputs. In this paper, we propose a general learning framework for joint 2d-3d scene understanding by selecting informative virtual 2d views of the underlying 3d scene. We then feed both the 3d geometry and the generated virtual 2d views into any joint 2d-3d-input or pure 3d-input based deep neural models for improving 3d scene understanding. Specifically, we generate virtual 2d views based on an information score map learned from the current 3d scene semantic segmentation results. To achieve this, we formalize the learning of the information score map as a deep reinforcement learning process, which rewards good predictions using a deep neural network. To obtain a compact set of virtual 2d views that jointly cover informative surfaces of the 3d scene as much as possible, we further propose an efficient greedy virtual view coverage strategy in the normal-sensitive 6d space, including 3-dimensional point coordinates and 3-dimensional normal. We have validated our proposed framework for various joint 2d-3d-input or pure 3d-input based deep neural models on two real-world 3d scene datasets, i.e., ScanNet v2 and S3dIS, and the results demonstrate that our method obtains a consistent gain over baseline models and achieves new top accuracy for joint 2d and 3d scene semantic segmentation.

关键词： Three-dimensional displays semantic segmentation Solid modeling semantics Geometry Task analysis deep reinforcement learning Virtual view selection 2d-3d joint learning deep reinforcement learning 3d semantic segmentation

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Efficient 3d Scene semantic segmentation via Active Learning on Rendered 2d Images

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 3521-3535页

作者： Rong, Mengqi Cui, Hainan Shen, Shuhan Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CASIA SenseTime Res Grp Beijing 100190 Peoples R China

Inspired by Active Learning and 2d-3d semantic fusion, we proposed a novel framework for 3d scene semantic segmentation based on rendered 2d images, which could efficiently achieve semantic segmentation of any large-scale 3d scene with only a few 2d image annotations. In our framework, we first render perspective images at certain positions in the 3d scene. Then we continuously fine-tune a pre-trained network for image semantic segmentation and project all dense predictions to the 3d model for fusion. In each iteration, we evaluate the 3d semantic model and re-render images in several representative areas where the 3d segmentation is not stable and send them to the network for training after annotation. Through this iterative process of rendering-segmentation-fusion, it can effectively generate difficult-to-segment image samples in the scene, while avoiding complex 3d annotations, so as to achieve label-efficient 3d scene segmentation. Experiments on three large-scale indoor and outdoor 3d datasets demonstrate the effectiveness of the proposed method compared with other state-of-the-art.

关键词： 3d semantic segmentation active learning rendered multi-view images

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deep Learning-Based 3d Instance and semantic segmentation: A Review

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Journal on Artificial Intelligence 2022年第2期4卷 99-114页

作者： Siddiqui Muhammad Yasir Hyunsik Ahn Department of Robot System Engineering Tongmyong UniversityBusan48520Korea School of Artificial Intelligence Tongmyong UniversityBusan48520Korea

The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3d *** is challenging with point cloud data due to substantial redundancy,fluctuating sample density and lack of apparent *** research area has a wide range of robotics applications,including intelligent vehicles,autonomous mapping and navigation.A number of researchers have introduced various methodologies and *** learning has been successfully used to a spectrum of 2d vision domains as a prevailing ***,due to the specific problems of processing point clouds with deep neural networks,deep learning on point clouds is still in its initial *** study examines many strategies that have been presented to 3d instance and semantic segmentation and gives a complete assessment of current developments in deep learning-based 3d *** these approaches’benefits,draw backs,and design mechanisms are studied and *** study evaluates the impact of various segmentation algorithms on competitiveness on various publicly accessible datasets,as well as the most often used pipelines,their advantages and limits,insightful findings and intriguing future research directions.

关键词： Artificial intelligence computer vision robot vision 3d instance segmentation 3d semantic segmentation 3d data deep learning point cloud mesh voxel RGB-d segmentation

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Seg-invRender: fusing semantic segmentation based on NeRF for inverse rendering considering shadows

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VISUAL COMPUTER 2025年第7期41卷 4851-4864页

作者： Wang, Jianuo Li, Huawei Chen, Yumin Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Inverse rendering remains a challenging problem in computer vision, but the recent advance in implicit neural rendering methods has introduced new ideas. Most current related works decompose the geometry, illumination and materials of a scene under an unknown lighting environment, but due to lack of explicit constraints on the material, the prediction of materials is easily interfered by shadows which may be misinterpreted as intrinsic materials. To address this issue, we propose an inverse rendering method that incorporates semantic segmentation. Considering obvious shadows in a scene, we aim to effectively mitigate inverse rendering's ill-posedness and decompose the scene's geometry, illumination, and materials more accurately. Our method first reconstructs sign distance function of the scene, based on which we can obtain a set of surface points for semantic labels on sparse-view images of the scene. We then project these points onto image plane to generate semantic labels on each view images of the scene, enabling sparsely supervised semantic segmentation of the multi-view images of the 3d scene without additional network training. Finally, considering a certain and limited number of material classes of the scene, we further use the results of semantic segmentation to explicitly constrain the materials prediction. Extensive experiments demonstrate that our method can effectively attenuate the influence of shadows on the material prediction when the scene has obvious shadows, leading to a more accurate prediction of illumination and materials. Our code and data are available at https://***/wjn-uo/Seg-InvRender.

关键词： Neural radiance field SdF Inverse rendering 3d reconstruction 3d semantic segmentation

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HFA-Net: hybrid feature-aware network for large-scale point cloud semantic segmentation

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第4期58卷 1-34页

作者： Wen, Changji Zhang, Long Ren, Junfeng Hong, Rundong Li, Chenshuang Yang, Ce Lv, Yanfeng Chen, Hongbing Yang, Ning Jilin Agr Univ Coll Informat & Technol Changchun Peoples R China Jiangsu Univ Minist Educ Key Lab Modern Agr Equipment & Technol Zhenjiang Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Minnesota Coll Food Agr & Nat Resource Sci St Paul MN USA

semantic segmentation of large-scale point clouds in 3d computer vision is a challenging problem. Existing feature extraction modules often emphasize learning local geometry while not giving adequate consideration to the integration of color information. This limitation prevents the network from thoroughly learning local features, thereby impacting segmentation accuracy. In this study, we propose three modules for robust feature extraction and aggregation, forming a novel point cloud segmentation network (HFA-Net) for large-scale point cloud semantic segmentation. First, we introduce the Hybrid Feature Extraction Component (HFEC) and the Hybrid Bilateral Enhancement Component (HBAC) to comprehensively extract and enhance the geometric, color, and semantic information of point clouds. Second, we incorporate the Ternary-distance Attention Pooling (TdAP) module, which leverages trilateral distances to further refine the network's focus on various features, enabling it to emphasize both locally important features and broader local neighborhoods. These modules are stacked into dense residual components to expand the network's receptive field. Our experiments on several large-scale benchmark datasets, including semantic3d, Toronto3d, S3dIS and LASdU demonstrate the effectiveness of HFA-Net when compared to state-of-the-art networks.

关键词： Point cloud 3d semantic segmentation Large-scale scene Hybrid feature extraction Bilateral enhancement

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