Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

• URL: http://arxiv.org/abs/2009.03137v3
• Date: Tue, 6 Apr 2021 04:19:34 GMT
• Title: Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges
• Authors: Qingyong Hu, Bo Yang, Sheikh Khalid, Wen Xiao, Niki Trigoni, Andrew Markham
• Abstract summary: We present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points. Our dataset consists of large areas from three UK cities, covering about 7.6 km2 of the city landscape. We evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results.
• Score: 52.624157840253204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets. However, publicly available datasets are either in relative small spatial scales or have limited semantic annotations due to the expensive cost of data acquisition and data annotation, which severely limits the development of fine-grained semantic understanding in the context of 3D point clouds. In this paper, we present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is three times the number of labeled points than the existing largest photogrammetric point cloud dataset. Our dataset consists of large areas from three UK cities, covering about 7.6 km^2 of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes. We extensively evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results. In particular, we identify several key challenges towards urban-scale point cloud understanding. The dataset is available at https://github.com/QingyongHu/SensatUrban.

Related papers

• MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations [55.022519020409405]
  This paper builds the first largest ever multi-modal 3D scene dataset and benchmark with hierarchical grounded language annotations, MMScan. The resulting multi-modal 3D dataset encompasses 1.4M meta-annotated captions on 109k objects and 7.7k regions as well as over 3.04M diverse samples for 3D visual grounding and question-answering benchmarks.
  arXiv  Detail & Related papers  (2024-06-13T17:59:30Z)
• CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data [15.526523262690965]
  We introduce the CityRefer dataset for city-level visual grounding. The dataset consists of 35k natural language descriptions of 3D objects appearing in SensatUrban city scenes and 5k landmarks labels synchronizing with OpenStreetMap.
  arXiv  Detail & Related papers  (2023-10-28T18:05:32Z)
• Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting [64.7364925689825]
  Argoverse 2 (AV2) is a collection of three datasets for perception and forecasting research in the self-driving domain. The Lidar dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. The Motion Forecasting dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene.
  arXiv  Detail & Related papers  (2023-01-02T00:36:22Z)
• SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds [52.624157840253204]
  We introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km2. Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset.
  arXiv  Detail & Related papers  (2022-01-12T14:48:11Z)
• H3D: Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and textured Meshes from UAV LiDAR and Multi-View-Stereo [4.263987603222371]
  This paper introduces a 3D dataset which is unique in three ways. It depicts the village of Hessigheim (Germany) henceforth referred to as H3D. It is designed for promoting research in the field of 3D data analysis on one hand and to evaluate and rank emerging approaches.
  arXiv  Detail & Related papers  (2021-02-10T09:33:48Z)
• Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset [67.44497676652173]
  We introduce a new outdoor urban 3D pointcloud dataset, covering a total area of 2.7 $km2$, sampled from three Swiss cities. The dataset is manually annotated for semantic segmentation with per-point labels, and is built using photogrammetry from images acquired by multirotors equipped with high-resolution cameras.
  arXiv  Detail & Related papers  (2020-12-23T21:48:47Z)
• Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene [76.4183572058063]
  We present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks. The dataset has been point-wisely annotated with both hierarchical and instance-based labels. We formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
  arXiv  Detail & Related papers  (2020-08-11T19:10:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.