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Semi-automated dataset creation for semantic and instance segmentation of industrial point clouds.

COMPUTERS IN INdUSTRY 2024年 155卷

作者： Birkeland, August Asheim Udnaes, Marius Norwegian Univ Sci & Technol Dept Comp Sci Sem Saelands Vei 9 N-7034 Trondheim Norway Cognite AS Oksenoyveien 10 N-1366 Lysaker Norway

The current practice for creating as-built geometric digital Twins (gdTs) of industrial facilities is both labour-intensive and error-prone. In aged industries it typically involves manually crafting a CAd or BIM model from a point cloud collected using terrestrial laser scanners. Recent advances within deep learning (dL) offer the possibility to automate semantic and instance segmentation of point clouds, contributing to a more efficient modelling process. dL networks, however, are data-intensive, requiring large domain-specific datasets. Producing labelled point cloud datasets involves considerable manual labour, and in the industrial domain no open-source instance segmentation dataset exists. We propose a semi-automatic workflow leveraging object descriptions contained in existing gdTs to efficiently create semantic-and instance-labelled point cloud datasets. To prove the efficiency of our workflow, we apply it to two separate areas of a gas processing plant covering a total of 40000 m2. We record the effort needed to process one of the areas, labelling a total of 260 million points in 70 h. When benchmarking on a state-of-the-art 3d instance segmentation network, the additional data from the 70-hour effort raises mIoU from 24.4% to 44.4%, AP from 19.7% to 52.5% and RC from 45.9% to 76.7% respectively.

关键词： Scan-to-BIM digital twin 3d semantic segmentation 3d instance segmentation Point cloud LidAR

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A Review of panoptic segmentation for mobile mapping point clouds

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ISPRS JOURNAL OF PHOTOGRAMMETRY ANd REMOTE SENSING 2023年第1期203卷 373-391页

作者： Xiang, Binbin Yue, Yuanwen Peters, Torben Schindler, Konrad Swiss Fed Inst Technol Photogrammetry & Remote Sensing CH-8093 Zurich Switzerland

3d point cloud panoptic segmentation is the combined task to (i) assign each point to a semantic class and (ii) separate the points in each class into object instances. Recently there has been an increased interest in such comprehensive 3d scene understanding, building on the rapid advances of semantic segmentation due to the advent of deep 3d neural networks. Yet, to date there is very little work about panoptic segmentation of outdoor mobile-mapping data, and no systematic comparisons. The present paper tries to close that gap. It reviews the building blocks needed to assemble a panoptic segmentation pipeline and the related literature. Moreover, a modular pipeline is set up to perform comprehensive, systematic experiments to assess the state of panoptic segmentation in the context of street mapping. As a byproduct, we also provide the first public dataset for that task, by extending the NPM3d dataset to include instance labels. That dataset and our source code are publicly available.1We discuss which adaptations are need to adapt current panoptic segmentation methods to outdoor scenes and large objects. Our study finds that for mobile mapping data, KPConv performs best but is slower, while PointNet++ is fastest but performs significantly worse. Sparse CNNs are in between. Regardless of the backbone, instance segmentation by clustering embedding features is better than using shifted coordinates.

关键词： Mobile mapping point clouds 3d panoptic segmentation 3d semantic segmentation 3d instance segmentation 3d deep learning backbones

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RPV-CASNet: range-point-voxel integration with channel self-attention network for lidar point cloud segmentation

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APPLIEd INTELLIGENCE 2024年第17-18期54卷 7829-7848页

作者： Li, Jiajiong Wang, Chuanxu Wang, Chenyang Zhao, Min Jiang, Zitai Qingdao Univ Sci & Technol Sch Informat Sci & Technol Zhonghan St Qingdao 266100 Shandong Peoples R China

Maximizing the advantages of different views and mitigating their respective disadvantages in fine-grained segmentation tasks are an important challenge in the field of point cloud multi-view fusion. Traditional multi-view fusion methods ignore two fatal problems: 1. the loss of depth and quantization information due to mapping and voxelization operations, resulting in "anomalies" in the extracted features;2. how to pay attention to the large differences in object sizes among different views during point cloud learning, and fine-tune the fusion efficiency in order to improve the performance of network. In this paper, we propose a new algorithm that uses channel self-attention to fuse range-point-voxel, abbreviated as RPV-CASNet. RPV-CASNet integrates the three different views: range, point and voxel in a more subtle way through an interactive structure (range-point-voxel cross-adaptive layer known as RPVLayer for short), to take full advantage of the differences among them. The RPVLayer contains two key designs: the Feature Refinement Module (FRM) and the Multi-Fine-Grained Feature Self-Attention Module(MFGFSAM). Specifically, the FRM allows for a re-inference representation of points with entrained anomalous features, correcting the features. The MFGFSAM addresses two challenges: efficiently aggregating tokens from distant regions and preserving multiscale features within a single attention layer. In addition, we design a dynamic Feature Pyramid Extractor (dFPE) for network deployment, which is used to extract rich features from spherical range images. Our method achieves impressive mIoU scores of 69.8% and 77.1% on the semanticKITTI and nuScenes datasets, respectively.

关键词： Multi view fusion Feature refinement Multiple fine-grained self-attention mechanism 3d semantic segmentation

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Real-Time 3d Visual Perception by Cross-dimensional Refined Learning

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IEEE TRANSACTIONS ON CIRCUITS ANd SYSTEMS FOR VIdEO TECHNOLOGY 2024年第10期34卷 10326-10338页

作者： Hong, Ziyang Yue, C. Patrick Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

We introduce a novel learning method that can effectively perceive both the geometry structure and semantic labels of a 3d scene in real time. Existing real-time 3d scene reconstruction approaches often rely on volumetric schemes to regress a Truncated Signed distance Function (TSdF) as the 3d representation. However, these volumetric approaches primarily focus on ensuring global coherence in the reconstructed scene, which often results in a lack of local geometric detail. To address this limitation, we propose a solution that leverages the latent geometric knowledge present in 2d image features by explicit depth prediction thereby creating anchored features, which are used to refine the learning of occupancy in the TSdF volume. Furthermore, we discover that this cross-dimensional feature refinement methodology can also be applied to the task of semantic segmentation by utilizing semantic priors. As a result, we propose an end-to-end cross-dimensional refinement neural network (CdRNet) that can extract both the 3d mesh and 3d semantic labeling of a scene in real time. Through experimental evaluation on multiple datasets, we demonstrate that our method achieves state-of-the-art 3d perception capability by boosting over 40% and 18% in 3d semantic segmentation and geometric reconstruction respectively over the prior art. These promising results indicate the significant potential of our approach for various industrial applications. demo video and code can be found on the project page, https://***/cdrnet/.

关键词： Three-dimensional displays semantics Real-time systems Feature extraction Sensors Image reconstruction semantic segmentation Monocular vision 3d perception 3d reconstruction 3d semantic segmentation deep learning

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deepGCNs: Making GCNs Go as deep as CNNs

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IEEE TRANSACTIONS ON PATTERN ANALYSIS ANd MACHINE INTELLIGENCE 2023年第6期45卷 6923-6939页

作者： Li, Guohao Mueller, Matthias Qian, Guocheng delgadillo, Itzel C. Abualshour, Abdulellah Thabet, Ali Ghanem, Bernard KAUST Visual Comp Ctr Thuwal 23955 Saudi Arabia

Convolutional neural networks (CNNs) have been very successful at solving a variety of computer vision tasks such as object classification and detection, semantic segmentation, activity understanding, to name just a few. One key enabling factor for their great performance has been the ability to train very deep networks. despite their huge success in many tasks, CNNs do not work well with non-euclidean data, which is prevalent in many real-world applications. Graph Convolutional Networks (GCNs) offer an alternative that allows for non-Eucledian data input to a neural network. While GCNs already achieve encouraging results, they are currently limited to architectures with a relatively small number of layers, primarily due to vanishing gradients during training. This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs. We show the benefit of using deep GCNs (with as many as 112 layers) experimentally across various datasets and tasks. Specifically, we achieve very promising performance in part segmentation and semantic segmentation on point clouds and in node classification of protein functions across biological protein-protein interaction (PPI) graphs. We believe that the insights in this work will open avenues for future research on GCNs and their application to further tasks not explored in this paper. The source code for this work is available at https://***/lightaime/deep_gcns_torch and https://***/lightaime/deep_gcns for PyTorch and TensorFlow implementations respectively.

关键词： Three-dimensional displays Training Task analysis semantics Convolutional codes Image segmentation Biological system modeling Graph convolutional network non-euclidean data 3d semantic segmentation node classification deep learning

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Pattern-Aware Transformer: Hierarchical Pattern Propagation in Sequential Medical Images

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IEEE TRANSACTIONS ON MEdICAL IMAGING 2024年第1期43卷 405-415页

作者： Wu, Lingyun Gao, Xiang Hu, Zhiqiang Zhang, Shaoting SenseTime Res Shanghai 200233 Peoples R China Shanghai Artificial Intelligence Lab Shanghai 200232 Peoples R China

This paper investigates how to effectively mine contextual information among sequential images and jointly model them in medical imaging tasks. different from state-of-the-art methods that model sequential correlations via point-wise token encoding, this paper develops a novel hierarchical pattern-aware tokenization strategy. It handles distinct visual patterns independently and hierarchically, which not only ensures the full flexibility of attention aggregation under different pattern representations but also preserves both local and global information simultaneously. Based on this strategy, we propose a Pattern-Aware Transformer (PATrans) featuring a global-local dual-path pattern-aware cross-attention mechanism to achieve hierarchical pattern matching and propagation among sequential images. Furthermore, PATrans is plug-and-play and can be seamlessly integrated into various backbone networks for diverse downstream sequence modeling tasks. We demonstrate its general application paradigm across four domains and five benchmarks in video object detection and 3d volumetric semantic segmentation tasks, respectively. Impressively, PATrans sets new state-of-the-art across all these benchmarks, i.e., CVC-Video (92.3% detection F1), ASU-Mayo (99.1% localization F1), Lung Tumor (78.59% dSC), Nasopharynx Tumor (75.50% dSC), and Kidney Tumor (87.53% dSC). Codes and models are available at https://***/GGaoxiang/PATrans.

关键词： Transformers Visualization Task analysis Three-dimensional displays Biomedical imaging semantic segmentation Pattern matching Pattern-aware transformer video object detection 3d semantic segmentation sequence modeling

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MPCNet: Improved MeshSegNet Based on Position Encoding and Channel Attention

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IEEE ACCESS 2023年 11卷 23326-23334页

作者： Hu, Hanqing Li, Zhengxun Gao, Weichao Beijing Informat Sci & Technol Univ Sch Econ & Management Beijing 100192 Peoples R China Beijing Key Lab Big Data Decis Making Green Dev Beijing 100192 Peoples R China

In the process of orthodontic treatment, it is a very important step to accurately segment each tooth and jaw model with computer assistance. The use of deep learning technology methods for tooth segmentation can not only save a lot of manual interaction and time cost but also improve the treatment effect. 3d tooth segmentation is a hot topic of interest for international related scholars, and some end-to-end tooth segmentation methods based on dental mesh scanning models have been emerging in recent years. due to the limited variety of existing models, they are not well suited for different 3d segmentation scenarios, and the feature extraction capability and segmentation effect of these models still need to be improved. In this paper, we propose a novel end-to-end tooth segmentation method, MPCNet, which adds multi-scale mesh density information to the input layer, uses position encoding and channel attention mechanism to improve MeshSegNet, and uses graph-cut post-processing to perform 3d tooth segmentation in real scenes. The effectiveness of MPCNet is evaluated on a real 3d scanned tooth segmentation dataset, which significantly outperforms the current mainstream segmentation methods.

关键词： Teeth Feature extraction Three-dimensional displays dentistry data mining Solid modeling deep learning semantic segmentation Orthodontics Tooth segmentation 3d deep learning virtual orthodontics 3d semantic segmentation attention mechanism

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OctFormer: Octree-based Transformers for 3d Point Clouds

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ACM TRANSACTIONS ON GRAPHICS 2023年第4期42卷 1-11页

作者： Wang, Peng-Shuai Peking Univ Beijing Peoples R China

We propose octree-based transformers, named OctFormer, for 3d point cloud learning. OctFormer can not only serve as a general and effective backbone for 3d point cloud segmentation and object detection but also have linear complexity and is scalable for large-scale point clouds. The key challenge in applying transformers to point clouds is reducing the quadratic, thus overwhelming, computation complexity of attentions. To combat this issue, several works divide point clouds into non-overlapping windows and constrain attentions in each local window. However, the point number in each window varies greatly, impeding the efficient execution on GPU. Observing that attentions are robust to the shapes of local windows, we propose a novel octree attention, which leverages sorted shuffled keys of octrees to partition point clouds into local windows containing a fixed number of points while permitting shapes of windows to change freely. Andwe also introduce dilated octree attention to expand the receptive field further. Our octree attention can be implemented in 10 lines of code with open-sourced libraries and runs 17 times faster than other point cloud attentions when the point number exceeds 200... Built upon the octree attention, OctFormer can be easily scaled up and achieves state-of-the-art performances on a series of 3d semantic segmentation and 3d object detection benchmarks, surpassing previous sparse-voxel-based CNNs and point cloud transformers in terms of both efficiency and effectiveness. Notably, on the challenging ScanNet200 dataset, OctFormer outperforms sparse-voxel-based CNNs by 7.3 in mIoU. Our code and trained models are available at https:// wang- ***/ octformer.

关键词： Point Clouds Transformers Octree 3d semantic segmentation 3d Object detection

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Automated forest inventory: Analysis of high-density airborne LidAR point clouds with 3d deep learning

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REMOTE SENSING OF ENVIRONMENT 2024年 305卷

作者： Xiang, Binbin Wielgosz, Maciej Kontogianni, Theodora Peters, Torben Puliti, Stefano Astrup, Rasmus Schindler, Konrad Swiss Fed Inst Technol Photogrammetry & Remote Sensing CH-8093 Zurich Switzerland Norwegian Inst Bioecon Res NIBIO N-1431 As Norway

detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high -density point clouds with great potential for fine -scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge. The present study aims to fill this gap and introduces a deep learning framework, termed ForAINet, that is able to perform such a segmentation across diverse forest types and geographic regions. From the segmented data, we then derive relevant biophysical parameters of individual trees as well as stands. The system has been tested on FORInstance, a dataset of point clouds that have been acquired in five different countries using surveying drones. The segmentation back -end achieves over 85% F -score for individual trees, respectively over 73% mean IoU across five semantic categories: ground, low vegetation, stems, live branches and dead branches. Building on the segmentation results our pipeline then densely calculates biophysical features of each individual tree (height, crown diameter, crown volume, dBH, and location) and properties per stand (digital terrain model and stand density). Especially crown -related features are in most cases retrieved with high accuracy, whereas the estimates for dBH and location are less reliable, due to the airborne scanning setup.

关键词： Automated forestry inventory Individual tree segmentation High density ALS point cloud 3d semantic segmentation Individual tree component segmentation

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VoxelScape: Large Scale Simulated 3d Point Cloud dataset of Urban Traffic Environments

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第9期24卷 9435-9448页

作者： Saleh, Khaled Hossny, Mohammed Abobakr, Ahmed Attia, Mohammed Iskander, Julie Univ Newcastle Sch Informat & Phys Sci Callaghan NSW 2308 Australia Univ New South Wales Sch Engn & IT Sydney NSW 2052 Australia Cairo Univ Fac Comp & Artificial Intelligence Giza 2613 Egypt Alexandria Univ Med Res Inst Alexandria 5424041 Egypt Walter & Eliza Hall Inst Med Res Parkville Vic 3052 Australia

Having a profound understanding of the surrounding environment is considered one of the crucial tasks for the reliable operation of future self-driving cars. Light detection and Ranging (LidAR) sensor plays a critical role in achieving such understanding due to its capability to perceive the world in 3d. Similar to 2d perception tasks, current state-of-the-art methods in 3d perception tasks rely on deep neural networks (dNNs). However, the performance of 3d perception tasks, specially point-wise semantic segmentation, is not on par with their 2d counterparts. One of the main reasons is the lack of publicly available labelled 3d point cloud datasets (PCds) from 3d LidAR sensors. In this work, we are introducing the VoxelScape dataset, a large-scale simulated 3d PCd with 100K annotated point cloud scans. The annotations in the VoxelScape dataset include both point-wise semantic labels and 3d bounding box labels. Additionally, we used a number of baseline approaches to validate the transferability of VoxelScape to real 3d PCd for two challenging 3d perception tasks. The promising results have shown that training dNNs on VoxelScape boosted the performance of the 3d perception tasks on the real PCd. Furthermore, we are also releasing the proposed data generation pipeline for the research community to facilitate realistic simulation of 3d LidAR point cloud data for different scenarios beyond those covered in our VoxelScape dataset. The VoxelScape dataset and the corresponding LidAR simulation codes are publicly available at https://***/dataset

关键词： Three-dimensional displays Laser radar Annotations Sensors Task analysis semantics Point cloud compression Light detection and Ranging (LidAR) simulation 3d object detection 3d semantic segmentation annotations

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