BuildingNet: Learning to Label 3D Buildings

• URL: http://arxiv.org/abs/2110.04955v1
• Date: Mon, 11 Oct 2021 01:45:26 GMT
• Title: BuildingNet: Learning to Label 3D Buildings
• Authors: Pratheba Selvaraju, Mohamed Nabail, Marios Loizou, Maria Maslioukova, Melinos Averkiou, Andreas Andreou, Siddhartha Chaudhuri, Evangelos Kalogerakis
• Abstract summary: BuildingNet: (a) large-scale 3D building models whose exteriors consistently labeled, (b) a neural network that labels building analyzing and structural relations of their geometric primitives. The dataset covers categories, such as houses, churches, skyscrapers, town halls and castles.
• Score: 19.641000866952815
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce BuildingNet: (a) a large-scale dataset of 3D building models whose exteriors are consistently labeled, (b) a graph neural network that labels building meshes by analyzing spatial and structural relations of their geometric primitives. To create our dataset, we used crowdsourcing combined with expert guidance, resulting in 513K annotated mesh primitives, grouped into 292K semantic part components across 2K building models. The dataset covers several building categories, such as houses, churches, skyscrapers, town halls, libraries, and castles. We include a benchmark for evaluating mesh and point cloud labeling. Buildings have more challenging structural complexity compared to objects in existing benchmarks (e.g., ShapeNet, PartNet), thus, we hope that our dataset can nurture the development of algorithms that are able to cope with such large-scale geometric data for both vision and graphics tasks e.g., 3D semantic segmentation, part-based generative models, correspondences, texturing, and analysis of point cloud data acquired from real-world buildings. Finally, we show that our mesh-based graph neural network significantly improves performance over several baselines for labeling 3D meshes.

Related papers

• ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding [51.509115746992165]
  We introduce ARKit LabelMaker, the first large-scale, real-world 3D dataset with dense semantic annotations. We also push forward the state-of-the-art performance on ScanNet and ScanNet200 dataset with prevalent 3D semantic segmentation models.
  arXiv  Detail & Related papers  (2024-10-17T14:44:35Z)
• ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point Clouds [0.0]
  This paper introduces a semantically-enriched, photo-realistic 3D architectural models dataset and benchmark for semantic segmentation. It features 4 different building purposes of real-world buildings as well as an open architectural landscape in Hong Kong.
  arXiv  Detail & Related papers  (2024-06-03T14:02:23Z)
• AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration [69.21282992341007]
  Auto Synth automatically generates 3D training data for point cloud registration. We replace the point cloud registration network with a much smaller surrogate network, leading to a $4056.43$ speedup. Our results on TUD-L, LINEMOD and Occluded-LINEMOD evidence that a neural network trained on our searched dataset yields consistently better performance than the same one trained on the widely used ModelNet40 dataset.
  arXiv  Detail & Related papers  (2023-09-20T09:29:44Z)
• Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds [4.38301148531795]
  Existing datasets for 3D modeling mainly focus on common objects such as furniture or cars. We present a urban-scale dataset consisting of more than 160 thousands buildings along with corresponding point clouds, mesh and wire-frame models, covering 16 cities in Estonia about 998 Km2. Experimental results indicate that Building3D has challenges of high intra-class variance, data imbalance and large-scale noises.
  arXiv  Detail & Related papers  (2023-07-21T21:38:57Z)
• Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery [20.001807614214922]
  Building 3D reconstruction from remote sensing images has a wide range of applications in smart cities, photogrammetry and other fields. We propose an efficient DSM estimation-driven reconstruction framework (Building3D) to reconstruct 3D building models from the input single-view remote sensing image. Our Building3D is rooted in the SFFDE network for building elevation prediction, synchronized with a building extraction network for building masks, and then sequentially performs point cloud reconstruction, surface reconstruction (or CityGML model reconstruction)
  arXiv  Detail & Related papers  (2023-01-11T17:20:30Z)
• FoldingNet Autoencoder model to create a geospatial grouping of CityGML building dataset [0.0]
  This study uses 'FoldingNet,' a 3D autoencoder, to generate the latent representation of each building from the LoD 2 CityGML dataset. The efficacy of the embeddings is analyzed by dataset reconstruction, latent spread visualization, and hierarchical clustering methods. A geospatial model is created to iteratively find the geographical groupings of buildings.
  arXiv  Detail & Related papers  (2022-12-28T17:16:23Z)
• 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)
• Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture [6.383666639192481]
  We create a synthetic data generation pipeline that generates an arbitrary amount of 3D data along with the associated 2D and 3D annotations. The variety of annotations, the flexibility to customize the generated building and dataset parameters make this framework suitable for multiple deep learning tasks. All code and data are made publicly available.
  arXiv  Detail & Related papers  (2021-04-26T13:32:03Z)
• Unsupervised Learning of 3D Object Categories from Videos in the Wild [75.09720013151247]
  We focus on learning a model from multiple views of a large collection of object instances. We propose a new neural network design, called warp-conditioned ray embedding (WCR), which significantly improves reconstruction. Our evaluation demonstrates performance improvements over several deep monocular reconstruction baselines on existing benchmarks.
  arXiv  Detail & Related papers  (2021-03-30T17:57:01Z)
• 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)
• Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges [52.624157840253204]
  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.
  arXiv  Detail & Related papers  (2020-09-07T14:47:07Z)

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.