Point2Building: Reconstructing Buildings from Airborne LiDAR Point Clouds

• URL: http://arxiv.org/abs/2403.02136v2
• Date: Mon, 29 Jul 2024 10:43:09 GMT
• Title: Point2Building: Reconstructing Buildings from Airborne LiDAR Point Clouds
• Authors: Yujia Liu, Anton Obukhov, Jan Dirk Wegner, Konrad Schindler,
• Abstract summary: We present a learning-based approach to reconstruct buildings as 3D polygonal meshes from airborne LiDAR point clouds. Our model learns directly from the point cloud data, thereby reducing error propagation and increasing the fidelity of the reconstruction. We experimentally validate our method on a collection of airborne LiDAR data of Zurich, Berlin and Tallinn.
• Score: 23.897507889025817
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a learning-based approach to reconstruct buildings as 3D polygonal meshes from airborne LiDAR point clouds. What makes 3D building reconstruction from airborne LiDAR hard is the large diversity of building designs and especially roof shapes, the low and varying point density across the scene, and the often incomplete coverage of building facades due to occlusions by vegetation or to the viewing angle of the sensor. To cope with the diversity of shapes and inhomogeneous and incomplete object coverage, we introduce a generative model that directly predicts 3D polygonal meshes from input point clouds. Our autoregressive model, called Point2Building, iteratively builds up the mesh by generating sequences of vertices and faces. This approach enables our model to adapt flexibly to diverse geometries and building structures. Unlike many existing methods that rely heavily on pre-processing steps like exhaustive plane detection, our model learns directly from the point cloud data, thereby reducing error propagation and increasing the fidelity of the reconstruction. We experimentally validate our method on a collection of airborne LiDAR data of Zurich, Berlin and Tallinn. Our method shows good generalization to diverse urban styles.

Related papers

• APC2Mesh: Bridging the gap from occluded building façades to full 3D models [5.113739955215433]
  We propose APC2Mesh which integrates point completion into a 3D reconstruction pipeline. Specifically, we leveraged complete points generated from occluded ones as input to a linearized skip attention-based deformation network for 3D mesh reconstruction.
  arXiv  Detail & Related papers  (2024-04-03T01:29:30Z)
• Few-shot point cloud reconstruction and denoising via learned Guassian splats renderings and fine-tuned diffusion features [52.62053703535824]
  We propose a method to reconstruct point clouds from few images and to denoise point clouds from their rendering. To improve reconstruction in constraint settings, we regularize the training of a differentiable with hybrid surface and appearance. We demonstrate how these learned filters can be used to remove point cloud noise coming without 3D supervision.
  arXiv  Detail & Related papers  (2024-04-01T13:38:16Z)
• Patch-Wise Point Cloud Generation: A Divide-and-Conquer Approach [83.05340155068721]
  We devise a new 3d point cloud generation framework using a divide-and-conquer approach. All patch generators are based on learnable priors, which aim to capture the information of geometry primitives. Experimental results on a variety of object categories from the most popular point cloud dataset, ShapeNet, show the effectiveness of the proposed patch-wise point cloud generation.
  arXiv  Detail & Related papers  (2023-07-22T11:10:39Z)
• StarNet: Style-Aware 3D Point Cloud Generation [82.30389817015877]
  StarNet is able to reconstruct and generate high-fidelity and even 3D point clouds using a mapping network. Our framework achieves comparable state-of-the-art performance on various metrics in the point cloud reconstruction and generation tasks.
  arXiv  Detail & Related papers  (2023-03-28T08:21:44Z)
• AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers [94.11915008006483]
  We present a new method that reformulates point cloud completion as a set-to-set translation problem. We design a new model, called PoinTr, which adopts a Transformer encoder-decoder architecture for point cloud completion. Our method attains 6.53 CD on PCN, 0.81 CD on ShapeNet-55 and 0.392 MMD on real-world KITTI.
  arXiv  Detail & Related papers  (2023-01-11T16:14:12Z)
• City3D: Large-scale Urban Reconstruction from Airborne Point Clouds [3.7422375336465037]
  We present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. Based on the observation that urban buildings typically consist of planar roofs connected with vertical walls to the ground, we propose an approach to infer the vertical walls directly from the data. Experiments on various large-scale airborne point clouds have demonstrated that the method is superior to the state-of-the-art methods in terms of reconstruction accuracy and robustness.
  arXiv  Detail & Related papers  (2022-01-25T12:41:11Z)
• Automated LoD-2 Model Reconstruction from Very-HighResolution Satellite-derived Digital Surface Model and Orthophoto [1.2691047660244335]
  We propose a model-driven method that reconstructs LoD-2 building models following a "decomposition-optimization-fitting" paradigm. Our proposed method has addressed a few technical caveats over existing methods, resulting in practically high-quality results.
  arXiv  Detail & Related papers  (2021-09-08T19:03:09Z)
• PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers [81.71904691925428]
  We present a new method that reformulates point cloud completion as a set-to-set translation problem. We also design a new model, called PoinTr, that adopts a transformer encoder-decoder architecture for point cloud completion. Our method outperforms state-of-the-art methods by a large margin on both the new benchmarks and the existing ones.
  arXiv  Detail & Related papers  (2021-08-19T17:58:56Z)
• ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds [78.25501874120489]
  We develop shape-aware adversarial 3D point cloud attacks by leveraging the learned latent space of a point cloud auto-encoder. Different from prior works, the resulting adversarial 3D point clouds reflect the shape variations in the 3D point cloud space while still being close to the original one.
  arXiv  Detail & Related papers  (2020-05-24T00:03:27Z)
• Deep Learning Guided Building Reconstruction from Satellite Imagery-derived Point Clouds [39.36437891978871]
  We present a reliable and effective approach for building model reconstruction from the point clouds generated from satellite images. Specifically, a deep-learning approach is adopted to distinguish the shape of building roofs in complex and yet noisy scenes. As the first effort to address the public need of large scale city model generation, the development is deployed as open source software.
  arXiv  Detail & Related papers  (2020-05-19T05:38:06Z)
• Curved Buildings Reconstruction from Airborne LiDAR Data by Matching and Deforming Geometric Primitives [13.777047260469677]
  We propose a new framework for curved building reconstruction via assembling and deforming geometric primitives. The presented framework is validated on several highly curved buildings collected by various LiDAR in different cities.
  arXiv  Detail & Related papers  (2020-03-22T16:05:10Z)

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.