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Context-Aware 3d Point Cloud semantic segmentation With Plane Guidance

IEEE TRANSACTIONS ON MULTIMEdIA 2023年 25卷 6653-6664页

作者： Weng, Tingyu Xiao, Jun Yan, Feilong Jiang, Haiyong Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Huya Live Guangzhou 511442 Peoples R China

Point cloud segmentation is fundamental in understanding 3d environments. However, most existing methods usually perform poorly on identifying boundaries of touching objects and large surfaces of objects. Planes in a scene usually act as supporting surfaces to separate touching objects and provide geometry priors to group points on a large surface as shown in Fig. 1. Besides, planes can roughly represent the structure of a scene, and are more efficient to encode holistic scene contexts than large scale point clouds. In light of the above advantages, we advise a plane-assisted module, coined 3d-PAM, to enhance semantic segmentation of touching objects and large surface objects. 3d-PAM consists of a plane separation network (PS-Net) and a plane relation network (PR-Net). PS-Net focuses on learning features that can robustly separate touching objects, e.g., a chair on a floor, as well as capture plane-based geometry priors to group points on a large plane, e.g., points of a desk. PR-Net encodes mutual plane relations as a proxy of a scene structure to capture holistic contexts. 3d-PAM is designed as a plug-and-play module so that it can be easily plugged into any off-the-shelf semantic segmentation network. Extensive experiments demonstrate that the method achieves large segmentation improvements on several backbones, and accomplishes superior results on most categories when using a RandLA-Net backbone (11/13 categories on S3dIS dataset and 15/20 categories on ScanNetv2 dataset). The project is available at GitHub https://***/windmillknight/ Context- Aware- 3dPoint- Cloud- semantic-segmentation-With- Plane- Guidance

关键词： Three-dimensional displays Point cloud compression Geometry Shape semantics Task analysis Network architecture 3d semantic segmentation holistic contexts point cloud plane geometry

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dPRNet: deep 3d Point Based Residual Network for semantic segmentation and Classification of 3d Point Clouds

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IEEE ACCESS 2019年 7卷 68892-68904页

作者： Arshad, Saira Shahzad, Muhammad Riaz, Qaiser Fraz, Muhammad Moazam Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad 44000 Pakistan Natl Ctr Artificial Intelligence Deep Learning Lab Islamabad 44000 Pakistan Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Alan Turing Inst British Lib London NW1 2DB England

Point clouds are an important type of geometric data obtained from a variety of 3d sensors. They do not have an explicit neighborhood structure and therefore several researchers often perform a voxelization step to obtain structured 3d neighborhood. This, however, comes with certain disadvantages, e.g., it makes the data unnecessarily voluminous, enforces additional computation effort and can potentially introduce quantization errors that may not only hinder in extracting implicit 3d shape information but also in capturing the essential data invariances for the required segmentation and recognition task. In this context, this paper addresses the highly challenging problem of semantic segmentation and 3d object recognition using raw unstructured 3d point cloud data. Specifically, the deep network architecture has been proposed which consists of a cascaded combination of 3d point-based residual networks for simultaneous semantic scene segmentation and object classification. It exploits the 3d point-based convolutions for representational learning from raw unstructured 3d point cloud data. The proposed architecture has a simple design, easier implementation, and the performance which is better than the existing state-of-the architectures particularly for semantic scene segmentation over three public datasets. The implementation and evaluation are made public here https://***/saira05/dPRNet.

关键词： Object recognition 3d point cloud deep residual learning 3d semantic segmentation

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3d-NOd: 3d new organ detection in plant growth by a spatiotemporal point cloud deep segmentation framework

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PLANT PHENOMICS 2025年第1期7卷

作者： Li, dawei Ahmed, Foysal Wang, Zhanjiang Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Donghua Univ State Key Lab Adv Fiber Mat SKLAFM Shanghai 201620 Peoples R China Donghua Univ Engn Res Ctr Digitized Text & Fash Technol Minist Educ Shanghai 201620 Peoples R China

Automatic plant growth monitoring is an important task in modern agriculture for maintaining high crop yield and boosting the breeding procedure. The advancement of 3d sensing technology has made 3d point clouds to be a better data form on presenting plant growth than images, as the new organs are easier identified in 3d space and the occluded organs in 2d can also be conveniently separated in 3d. despite the attractive characteristics, analysis on 3d data can be quite challenging. We present 3d-NOd, a framework to detect new organs from time-series 3d plant data by spatiotemporal point cloud deep semantic segmentation. The design of 3d-NOd framework drew inspiration from how a well-experienced human utilizes spatiotemporal information to identify growing buds from a plant at two different growth stages. In the training phase, by introducing the Backward & Forward Labeling, the Registration & Mix-up, and the Humanoid data Augmentation step, our backbone network can be trained to recognize growth events with organ correlation from both temporal and spatial domains. In testing, 3dNOd has shown better sensitivity at segmenting new organs against the conventional way of using a network to conduct direct semantic segmentation. On a time-series dataset containing multiple species, Our method reached a mean F1-measure at 88.13 % and a mean IoU at 80.68% on detecting both new and old organs with the dGCNN backbone.

关键词： 3d semantic segmentation Growth event detection Point cloud registration Plant phenotyping deep neural network

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deep learning based 3d segmentation in computer vision: A survey

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INFORMATION FUSION 2025年 115卷

作者： He, Yong Yu, Hongshan Liu, Xiaoyan Yang, Zhengeng Sun, Wei Anwar, Saeed Mian, Ajmal Hunan Univ Quanzhou Inst Ind Design & Machine Intelligence In Coll Elect & Informat Engn Sch Robot Lushan South Rd Changsha 410082 Hunan Peoples R China Hunan Normal Univ Lushan South Rd Changsha 410081 Hunan Peoples R China Australian Natl Univ Canberra ACT 2600 Australia Univ Western Australia 35 Stirling Hwy Perth WA 6009 Australia

3d segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving and robotics. It has received significant attention from the computer vision, graphics and machine learning communities. Conventional methods for 3d segmentation, based on hand-crafted features and machine learning classifiers, lack generalization ability. driven by their success in 2d computer vision, deep learning techniques have recently become the tool of choice for 3d segmentation tasks. This has led to an influx of many methods in the literature that have been evaluated on different benchmark datasets. Whereas survey papers on RGB-d and point cloud segmentation exist, there is a lack of a recent in-depth survey that covers all 3d data modalities and application domains. This paper fills the gap and comprehensively surveys the recent progress in deep learning-based 3d segmentation techniques. We cover over 230 works from the last six years, analyze their strengths and limitations, and discuss their competitive results on benchmark datasets. The survey provides a summary of the most commonly used pipelines and finally highlights promising research directions for the future.

关键词： Computer vision deep learning deep neural network 3d semantic segmentation 3d instance segmentation 3d part segmentation

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AUTOMATEd TRAINING dATA CREATION FOR semantic segmentation OF 3d POINT CLOUdS

AUTOMATED TRAINING DATA CREATION FOR SEMANTIC SEGMENTATION O...

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ISPRS WG V/7 Conference on Measurement, Visualisation and Processing in BIM for design and Construction Management II

作者： de Geyter, Sam Bassier, Maarten Vergauwen, Maarten Katholieke Univ Leuven TC Construct Geomat Dept Civil Engn Fac Engn Technol Ghent Belgium MEET HET BV Mariakerke Belgium

The creation of as-built Building Information Modelling (BIM) models currently is mostly manual which makes it time consuming and error prone. A crucial step that remains to be automated is the interpretation of the point clouds and the modelling of the BIM geometry. Research has shown that despite the advancements in semantic segmentation, the deep Learning (dL) networks that are used in the interpretation do not achieve the necessary accuracy for market adoption. One of the main reasons is a lack of sufficient and representative labelled data to train these models. In this work, the possibility to use already conducted Scan-to-BIM projects to automatically generate highly needed training data in the form of labelled point clouds is investigated. More specifically, a pipeline is presented that uses real-world point clouds and their corresponding manually created BIM models. In doing so, realistic and representative training data is created. The presented paper is focussed on the semantic segmentation of 6 common structure BIM classes, representing the main structure of a building. The experiments show that the pipeline successfully creates new training data for a recent dL network.

关键词： Scan-to-BIM 3d semantic segmentation 3d training data Point cloud LIdAR

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A Multi-scale Network for semantic segmentation of 3d Point Clouds

A Multi-scale Network for Semantic Segmentation of 3D Point ...

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Chinese Automation Congress (CAC)

作者： He, Ying Xiao, Li Jiang, Yong Sun, Zhigang Wang, Zhuo Peng, Gang Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan Peoples R China

ISBN: (纸本)9781728176871

semantic segmentation is a fundamental operation in scene analysis. In this paper, an effective multiscale network for 3d point cloud semantic segmentation was introduced. By using a multiscale local feature extraction module which composed of four feature extractors of different scales in parallel, the generalizability of network for complex structures is enhanced effectively. To adaptively learn important feature channels, an attention mechanism is designed. Combining multiple features through skip connection, the network can preferably assign the semantic label for every point by exploiting global and local features. Experiments on 3d dataset (S3dIS) verify that our network is able to learn local region features, and the results are superior or comparable to the state-of-the-art.

关键词： multiscale network 3d semantic segmentation channel attention module

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3d CMM-Net with deeper Encoder for semantic segmentation of Brain Tumors in BraTS2021 Challenge 7th

3D CMM-Net with Deeper Encoder for Semantic Segmentation of ...

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7th International Brain Lesion Workshop (BrainLes)

作者： Choi, Yoonseok Al-Masni, Mohammed A. Kim, dong-Hyun Yonsei Univ Dept Elect & Elect Engn Coll Engn Seoul South Korea

ISBN: (纸本)9783031089992;9783031089985

We propose a 3d version of the Contextual Multi-scale Multi-level Network (3d CMM-Net) with deeper encoder depth for automated semantic segmentation of different brain tumors in the BraTS2021 challenge. The proposed network has the capability to extract and learn deeper features for the task of multi-class segmentation directly from 3d MRI data. The overall performance of the proposed network gave dice scores of 0.7557, 0.8060, and 0.8351 for enhancing tumor, tumor core, and whole tumor, respectively on the local-test dataset.

关键词： Brain tumor segmentation Pyramid pooling module U-Net Glioblastoma 3d semantic segmentation Multimodal MRI

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3d POINT CLOUd semantic segmentation BASEd ON dIFFUSION MOdEL 49

3D POINT CLOUD SEMANTIC SEGMENTATION BASED ON DIFFUSION MODE...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Liu, Chang Jiang, Aimin Tang, Yibin Zhu, Yanping Chen, Qi Hohai Univ Coll Informat Sci & Engn Changzhou Jiangsu Peoples R China Changzhou Univ Sch Microelectron & Control Engn Changzhou Jiangsu Peoples R China Changzhou Univ Sch Comp Sci & Artificial Intelligence Changzhou Jiangsu Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Point cloud segmentation plays a crucial role in extracting unique attributes and separating various objects, thereby enabling semantic comprehension and analysis. In this paper, we introduce a novel point cloud segmentation approach based on diffusion Probabilistic Network (ddPM). The proposed model treats points as particles undergoing diffusion towards a noise distribution, and a reverse diffusion process transforms this noise distribution into the desired shape. Leveraging a Markov diffusion model in the reverse process enables generating point clouds with more refined and specific topological structures. After the diffusion step, multiscale sampled features are fused to enhance the discriminative representation of 3d shapes. Objective and subjective experimental results demonstrate that our segmentation method outperforms state-of-the-art techniques in terms of evaluation metrics.

关键词： diffusion probabilistic model multiscale feature fusion point clouds 3d semantic segmentation

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RESSCAL3d++: JOINT ACQUISITION ANd semantic segmentation OF 3d POINT CLOUdS 31

RESSCAL3D++: JOINT ACQUISITION AND SEMANTIC SEGMENTATION OF ...

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2024 International Conference on Image Processing

作者： Royen, Remco Pataridis, Kostas van der Tempel, Ward Munteanu, Adrian Vrije Univ Brussel Dept ETRO Pleinlaan 2 B-1050 Brussels Belgium VoxelSensors Kantersteen 47 B-1000 Brussels Belgium

ISBN: (纸本)9798350349405;9798350349399

3d scene understanding is crucial for facilitating seamless interaction between digital devices and the physical world. Real-time capturing and processing of the 3d scene are essential for achieving this seamless integration. While existing approaches typically separate acquisition and processing for each frame, the advent of resolution-scalable 3d sensors offers an opportunity to overcome this paradigm and fully leverage the otherwise wasted acquisition time to initiate processing. In this study, we introduce VX-S3dIS, a novel point cloud dataset accurately simulating the behavior of a resolution-scalable 3d sensor. Additionally, we present RESSCAL3d++, an important improvement over our prior work, RESSCAL3d, by incorporating an update module and processing strategy. By applying our method to the new dataset, we practically demonstrate the potential of joint acquisition and semantic segmentation of 3d point clouds. Our resolution-scalable approach significantly reduces scalability costs from 2% to just 0.2% in mIoU while achieving impressive speed-ups of 15.6 to 63.9% compared to the non-scalable baseline. Furthermore, our scalable approach enables early predictions, with the first one occurring after only 7% of the total inference time of the baseline. The new VX-S3dIS dataset is available at https://***/remcoroyen/vx-s3dis.

关键词： Point clouds 3d semantic segmentation resolution scalability scalable data acquisition dataset

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3d Recurrent Neural Networks with Context Fusion for Point Cloud semantic segmentation 1

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15th European Conference on Computer Vision (ECCV)

作者： Ye, Xiaoqing Li, Jiamao Huang, Hexiao du, Liang Zhang, Xiaolin Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai Open Univ Sch Sci & Technol Shanghai Peoples R China

ISBN: (数字)9783030012342

ISBN: (纸本)9783030012342;9783030012335

semantic segmentation of 3d unstructured point clouds remains an open research problem. Recent works predict semantic labels of 3d points by virtue of neural networks but take limited context knowledge into consideration. In this paper, a novel end-to-end approach for unstructured point cloud semantic segmentation, named 3P-RNN, is proposed to exploit the inherent contextual features. First the efficient point-wise pyramid pooling module is investigated to capture local structures at various densities by taking multi-scale neighborhood into account. Then the two-direction hierarchical recurrent neural networks (RNNs) are utilized to explore long-range spatial dependencies. Each recurrent layer takes as input the local features derived from unrolled cells and sweeps the 3d space along two directions successively to integrate structure knowledge. On challenging indoor and outdoor 3d datasets, the proposed framework demonstrates robust performance superior to state-of-the-arts.

关键词： 3d semantic segmentation Unstructured point cloud Recurrent neural networks Pointwise pyramid pooling

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