The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo

10 February 2021

Papers citing "The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo"

7 / 7 papers shown

Title
Ground Awareness in Deep Learning for Large Outdoor Point Cloud Segmentation Kevin Qiu Dimitri Bulatov Dorota Iwaszczuk 3DPC 52 0 0 30 Jan 2025
Deep Learning on 3D Semantic Segmentation: A Detailed Review Thodoris Betsas Andreas Georgopoulos Anastasios Doulamis Pierre Grussenmeyer 3DV 3DPC 28 1 0 04 Nov 2024
HRHD-HK: A benchmark dataset of high-rise and high-density urban scenes for 3D semantic segmentation of photogrammetric point clouds Maosu Li Yijie Wu A. G. Yeh Fan Xue 3DV 3DPC 21 3 0 16 Jul 2023
Effective Utilisation of Multiple Open-Source Datasets to Improve Generalisation Performance of Point Cloud Segmentation Models Matthew Howe Boris Repasky Timothy Payne 3DPC 12 0 0 29 Nov 2022
One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning Puzuo Wang W. Yao Jiejing Shao 16 17 0 23 Nov 2022
SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds Qingyong Hu Bo Yang Sheikh Khalid W. Xiao Niki Trigoni Andrew Markham 3DPC 11 85 0 12 Jan 2022
3D Instance Segmentation of MVS Buildings Jiazhou Chen Yanghui Xu Shufang Lu Ronghua Liang Liangliang Nan ISeg 3DV 16 23 0 18 Dec 2021