SEMANTIC3D.NET: A NEW LARGE-SCALE POINT CLOUD CLASSIFICATION BENCHMARK

382 citations as recorded by crossref.

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273. A Survey of Label-Efficient Deep Learning for 3D Point Clouds A. Xiao et al.
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275. PointMCD: Boosting Deep Point Cloud Encoders via Multi-View Cross-Modal Distillation for 3D Shape Recognition Q. Zhang et al.
276. A Comprehensive Framework for Transportation Infrastructure Digitalization: TJYRoad-Net for Enhanced Point Cloud Segmentation Z. Yang et al.
277. Hierarchical Weight Averaging for Deep Neural Networks X. Gu et al.
278. Thiruvananthapuram Aerial LiDAR Dataset (TALD): A Benchmark for Complex Urban Point Cloud J. Vijaywargiya & A. Ramiya
279. Exploiting Structured CNNs for Semantic Segmentation of Unstructured Point Clouds from LiDAR Sensor M. Ibrahim et al.
280. A GCN-Based Method for Extracting Power Lines and Pylons From Airborne LiDAR Data W. Li et al.
281. UrbanLF: A Comprehensive Light Field Dataset for Semantic Segmentation of Urban Scenes H. Sheng et al.
282. MFFRand: Semantic Segmentation of Point Clouds Based on Multi-Scale Feature Fusion and Multi-Loss Supervision Z. Miao et al.
283. 2D–3D fusion approach for improved point cloud segmentation H. Yue et al.
284. Revue des descripteurs tridimensionnels (3D) pour la catégorisation des nuages de points acquis avec un système LiDAR de télémétrie mobile S. Daniel
285. A multi-scale fully convolutional network for semantic labeling of 3D point clouds M. Yousefhussien et al.
286. A multi-granularity semisupervised active learning for point cloud semantic segmentation S. Ye et al.
287. Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data X. Wei et al.
288. An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios E. Özdemir et al.
289. Building Extraction from Terrestrial Laser Scanning Data with Density of Projected Points on Polar Grid and Adaptive Threshold M. Chen et al.
290. A self-attention based global feature enhancing network for semantic segmentation of large-scale urban street-level point clouds Q. Chen et al.
291. One Class One Click: Quasi scene-level weakly supervised point cloud semantic segmentation with active learning P. Wang et al.
292. Unsupervised Building Instance Segmentation of Airborne LiDAR Point Clouds for Parallel Reconstruction Analysis Y. Zhang et al.
293. PReFormer: A memory-efficient transformer for point cloud semantic segmentation P. Akwensi et al.
294. Vegetation segmentation using oblique photogrammetry point clouds based on RSPT network H. Hu et al.
295. MuSic-UDF: Learning Multi-Scale dynamic grid representation for high-fidelity surface reconstruction from point clouds C. Jin et al.
296. Superpoint Transformer–Based Bridge Component Recognition Using UAV-Mounted LiDAR and Synthetic Point Cloud Generation Y. Kim et al.
297. Deep learning-based fast prediction of flow field around multiple bluff bodies P. Lin et al.
298. Semantic Context Encoding for Accurate 3D Point Cloud Segmentation H. Liu et al.
299. An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style Translation M. Chen et al.
300. FFA-Net: fast feature aggregation network for 3D point cloud segmentation R. Cheng et al.
301. Large-scale point cloud contour extraction via 3D guided multi-conditional generative adversarial network W. Zhang et al.
302. ResDLPS-Net: Joint residual-dense optimization for large-scale point cloud semantic segmentation J. Du et al.
303. Optimal LiDAR Data Resolution Analysis for Object Classification M. Darrah et al.
304. MVF-CNN: Fusion of Multilevel Features for Large-Scale Point Cloud Classification Y. Li et al.
305. 3D Semantic Scene Completion: A Survey L. Roldão et al.
306. Unsupervised Domain Transfer for Object Classification in 3D Point Clouds via Hierarchical Prompt Learning H. Luo et al.
307. Lidar Mapping Optimization Based on Lightweight Semantic Segmentation Z. Zhao et al.
308. 基于最小平方中值的点云平面拟合算法 王. Wang Yang et al.
309. Automated verticality quality assessment in prefabrication construction using laser scanning and synthetic point cloud data M. Wang et al.
310. Residue Assignment in Crystallographic Protein Electron Density Maps With 3D Convolutional Networks A. Godo et al.
311. 3DGENie: Synthetic point clouds for semantic segmentation in realistic virtual environments A. Yaghi et al.
312. Mesh Convolution With Continuous Filters for 3-D Surface Parsing H. Lei et al.
313. Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey L. Rauch & T. Braml
314. IGN: Instance-Guided Net for 3-D Instance Segmentation in Cluttered Scenes via Monocular Depth Sensor Z. Wan et al.
315. Graph Neural Networks in Point Clouds: A Survey D. Li et al.
316. SVLA: A compact supervoxel segmentation method based on local allocation H. Ni & X. Niu
317. Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators T. Shinohara et al.
318. RR-Net: 3-D Roof Reconstruction From Airborne LiDAR Point Clouds via Edge Segmentation and Wireframe Generation Y. Xu et al.
319. Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review S. Yang et al.
320. Integrating Deep Semantic Segmentation Into 3-D Point Cloud Registration A. Zaganidis et al.
321. Hybrid Local-Feature-Based Approach for Automated Rail Extraction from Lidar Data Y. Ren et al.
322. 基于点‑体素一致性约束的城市激光雷达点云分类 李. Li Huchen et al.
323. Deep Learning-Based Semantic Segmentation and Surface Reconstruction for Point Clouds of Offshore Oil Production Equipment Z. Wang et al.
324. PEMCNet: An Efficient Multi-Scale Point Feature Fusion Network for 3D LiDAR Point Cloud Classification G. Zhao et al.
325. NrtNet: An Unsupervised Method for 3D Non-Rigid Point Cloud Registration Based on Transformer X. Hu et al.
326. Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation M. Ibrahim et al.
327. 3D Point Cloud Dataset of Heavy Construction Equipment S. Park & S. Kim
328. Evaluation of Class Distribution and Class Combinations on Semantic Segmentation of 3D Point Clouds With PointNet E. Barnefske & H. Sternberg
329. Positive-Negative Receptive Field Reasoning for Omni-Supervised 3D Segmentation X. Tan et al.
330. PointLAE: A Point Cloud Semantic Segmentation Neural Network via Multifeature Aggregation for Large-Scale Application J. Li et al.
331. Backward Attentive Fusing Network With Local Aggregation Classifier for 3D Point Cloud Semantic Segmentation H. Shuai et al.
332. Semantic Segmentation of Terrestrial Laser Scanning Point Clouds Using Locally Enhanced Image-Based Geometric Representations Y. Cai et al.
333. Deep-Learning-Based Semantic Segmentation Approach for Point Clouds of Extra-High-Voltage Transmission Lines H. Yu et al.
334. CSPC-Dataset: New LiDAR Point Cloud Dataset and Benchmark for Large-Scale Scene Semantic Segmentation G. Tong et al.
335. Neighborhood co-occurrence modeling in 3D point cloud segmentation J. Gong et al.
336. A Comprehensive Review of Modern Object Segmentation Approaches Y. Wang et al.
337. Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification I. Farmakis et al.
338. Deep Learning on 3D Semantic Segmentation: A Detailed Review T. Betsas et al.
339. A machining feature recognition approach based on hierarchical neural network for multi-feature point cloud models X. Yao et al.
340. 3D CNN-Based Semantic Labeling Approach for Mobile Laser Scanning Data B. Nagy & C. Benedek
341. Distributed Optimization Based on Graph Filter for Ensuring Invariant Simplification of High-Volume Point Cloud X. Zhou et al.
342. Using Relative Projection Density for Classification of Terrestrial Laser Scanning Data with Unknown Angular Resolution M. Chen et al.
343. One Shot Learning for Edge Detection on Point Clouds Z. Tu et al.
344. Façade Separation in Ground-Based LiDAR Point Clouds Based on Edges and Windows S. Xia & R. Wang
345. Automated and Fast Point Cloud Labeling Method for Training Data Augmentation Using Scan-versus-BIM Approach S. Park et al.
346. A Survey of Synthetic Data Augmentation Methods in Machine Vision A. Mumuni et al.
347. Hausdorff point convolution with geometric priors L. Lin et al.
348. Deep Learning for Generating Time-of-Flight Camera Artifacts T. Müller et al.
349. A Review on Deep Learning Techniques for 3D Sensed Data Classification D. Griffiths & J. Boehm
350. SegTrans: Semantic Segmentation With Transfer Learning for MLS Point Clouds S. Shen et al.
351. Designing and Testing a UAV Mapping System for Agricultural Field Surveying M. Christiansen et al.
352. Channel Attention and Normal-Based Local Feature Aggregation Network (CNLNet): A Deep Learning Method for Predisaster Large-Scale Outdoor Lidar Semantic Segmentation C. Liu et al.
353. Multi-Scale Attentive Aggregation for LiDAR Point Cloud Segmentation X. Geng et al.
354. Weakly supervised point cloud segmentation via deep morphological semantic information embedding W. Xue et al.
355. Efficient Semantic Segmentation of Transmission Line Corridor Point Clouds via Transformer With Vector Aggregation H. Hu et al.
356. GL-Net: Semantic segmentation for point clouds of shield tunnel via global feature learning and local feature discriminative aggregation J. Li et al.
357. Rethinking Design and Evaluation of 3D Point Cloud Segmentation Models T. Zoumpekas et al.
358. Merge-Swap Optimization Framework for Supervoxel Generation from Three-Dimensional Point Clouds Y. Xiao et al.
359. Automatically Annotated Dataset of a Ground Mobile Robot in Natural Environments via Gazebo Simulations M. Sánchez et al.
360. Shield Tunnel Dislocation Detection Method Based on Semantic Segmentation and Bolt Hole Positioning of MLS Point Cloud H. Cui et al.
361. Coaxiality prediction for aeroengines precision assembly based on geometric distribution error model and point cloud deep learning K. Shang et al.
362. Low-Overlap Registration of Multi-Source LiDAR Point Clouds in Urban Scenes Through Dual-Stage Feature Pruning and Progressive Hierarchical Methods K. Ma et al.
363. An Accurate and Efficient Supervoxel Re-Segmentation Approach for Large-Scale Point Clouds Using Plane Constraints B. Lai et al.
364. Semantics for Robotic Mapping, Perception and Interaction: A Survey S. Garg et al.
365. KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D Y. Liao et al.
366. Context-Aware 3D Point Cloud Semantic Segmentation With Plane Guidance T. Weng et al.
367. Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds H. Luo et al.
368. RM3D: Robust Data-Efficient 3D Scene Parsing via Traditional and Learnt 3D Descriptors-Based Semantic Region Merging K. Liu
369. Optimising 3D point cloud semantic segmentation: ML and manual refinement in the UNESCO Saltaire Village V. Cotella et al.
370. Enhancing explainability of deep learning models for point cloud analysis: a focus on semantic segmentation F. Matrone et al.
371. PointDMS: An Improved Deep Learning Neural Network via Multi-Feature Aggregation for Large-Scale Point Cloud Segmentation in Smart Applications of Urban Forestry Management J. Li & J. Liu
372. Deep Learning for 3D Point Clouds: A Survey Y. Guo et al.
373. Deep learning techniques for point cloud tasks: a review X. Song et al.
374. Review: Deep Learning on 3D Point Clouds S. Bello et al.
375. Multilevel Ground Segmentation for 3-D Point Clouds of Outdoor Scenes Based on Shape Analysis Y. An et al.
376. A Large-Scale Network Construction and Lightweighting Method for Point Cloud Semantic Segmentation J. Han et al.
377. Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework Z. Zeng et al.
378. Semantic segmentation of large-scale point clouds with neighborhood uncertainty Y. Bao et al.
379. Towards Urban Scene Semantic Segmentation with Deep Learning from LiDAR Point Clouds: A Case Study in Baden-Württemberg, Germany Y. Zou et al.
380. Nested Architecture Search for Point Cloud Semantic Segmentation F. Yang et al.
381. RESEARCH ON CREATION OF IDENTIFICATION MODELS OF ROAD FEATURES USING HD MAPS R. IMAI et al.
382. Topology-aware multi-information fusion for object recognition Y. Wang et al.