A boundary-aware point clustering approach in Euclidean and embedding spaces for roof plane segmentation

• URL: http://arxiv.org/abs/2309.03722v2
• Date: Thu, 03 Oct 2024 03:46:05 GMT
• Title: A boundary-aware point clustering approach in Euclidean and embedding spaces for roof plane segmentation
• Abstract summary: Roof plane segmentation from airborne LiDAR point clouds is an important technology for 3D building model reconstruction. One of the key issues of plane segmentation is how to design powerful features that can distinguish adjacent planar patches. We propose a boundary-aware point clustering approach in Euclidean and embedding spaces constructed by a multi-task deep network for roof plane segmentation.
• Score: 9.944505802346733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Roof plane segmentation from airborne LiDAR point clouds is an important technology for 3D building model reconstruction. One of the key issues of plane segmentation is how to design powerful features that can exactly distinguish adjacent planar patches. The quality of point feature directly determines the accuracy of roof plane segmentation. Most of existing approaches use handcrafted features to extract roof planes. However, the abilities of these features are relatively low, especially in boundary area. To solve this problem, we propose a boundary-aware point clustering approach in Euclidean and embedding spaces constructed by a multi-task deep network for roof plane segmentation. We design a three-branch network to predict semantic labels, point offsets and extract deep embedding features. In the first branch, we classify the input data as non-roof, boundary and plane points. In the second branch, we predict point offsets for shifting each point toward its respective instance center. In the third branch, we constrain that points of the same plane instance should have the similar embeddings. We aim to ensure that points of the same plane instance are close as much as possible in both Euclidean and embedding spaces. However, although deep network has strong feature representative ability, it is still hard to accurately distinguish points near plane instance boundary. Therefore, we first group plane points into many clusters in the two spaces, and then we assign the rest boundary points to their closest clusters to generate final complete roof planes. In this way, we can effectively reduce the influence of unreliable boundary points. In addition, we prepare a synthetic dataset and two real datasets to train and evaluate our approach. The experiments results show that the proposed approach significantly outperforms the existing state-of-the-art approaches.

Related papers

• EdgeFormer: local patch-based edge detection transformer on point clouds [8.593893106675825]
  We present a learning-based edge detection network, named EdgeFormer, which mainly consists of two stages.<n>In the first stage, we construct local patch feature descriptors that describe the local neighborhood around each point.<n>In the second stage, we classify each point by analyzing the local patch feature descriptors generated in the first stage.
  arXiv  Detail & Related papers  (2026-04-23T07:57:04Z)
• RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation [7.458061089808731]
  Roof plane segmentation is one of the key procedures for reconstructing 3D building models at levels of detail (LoD) 2 and 3 from airborne light detection and ranging (LiDAR) point clouds.<n>Current approaches for roof plane segmentation rely on the manually designed or learned features followed by some specifically designed geometric clustering strategies.<n>We propose a novel edge-aware transformer-based network, named RoofSeg, for segmenting roof planes from LiDAR point clouds in a truly end-to-end manner.
  arXiv  Detail & Related papers  (2025-08-26T13:03:05Z)
• Fully-Geometric Cross-Attention for Point Cloud Registration [51.865371511201765]
  Point cloud registration approaches often fail when the overlap between point clouds is low due to noisy point correspondences. This work introduces a novel cross-attention mechanism tailored for Transformer-based architectures that tackles this problem. We integrate the Gromov-Wasserstein distance into the cross-attention formulation to jointly compute distances between points across different point clouds. At the point level, we also devise a self-attention mechanism that aggregates the local geometric structure information into point features for fine matching.
  arXiv  Detail & Related papers  (2025-02-12T10:44:36Z)
• Twin Deformable Point Convolutions for Point Cloud Semantic Segmentation in Remote Sensing Scenes [12.506628755166814]
  We propose novel convolution operators, termed Twin Deformable point Convolutions (TDConvs) These operators aim to achieve adaptive feature learning by learning deformable sampling points in the latitude-longitude plane and altitude direction. Experiments on existing popular benchmarks conclude that our TDConvs achieve the best segmentation performance.
  arXiv  Detail & Related papers  (2024-05-30T06:31:03Z)
• Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching [0.0]
  We propose a new technique, based on graph Laplacian eigenmaps, to match point clouds by taking into account fine local structures. To deal with the order and sign ambiguity of Laplacian eigenmaps, we introduce a new operator, called Coupled Laplacian. We show that the similarity between those aligned high-dimensional spaces provides a locally meaningful score to match shapes.
  arXiv  Detail & Related papers  (2024-02-27T10:10:12Z)
• PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction [72.75478398447396]
  We propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively. Considering the distance distribution of LiDAR point clouds, we construct the tri-perspective view in the cylindrical coordinate system. We employ spatial group pooling to maintain structural details during projection and adopt 2D backbones to efficiently process each TPV plane.
  arXiv  Detail & Related papers  (2023-08-31T17:57:17Z)
• Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point Clouds [0.5249805590164901]
  We propose a boundary-aware feature propagation mechanism to improve semantic segmentation near object boundaries. With one shared encoder, our network outputs (i) boundary localization, (ii) prediction of directions pointing to the object's interior, and (iii) semantic segmentation, in three parallel streams. Our proposed approach yields consistent improvements by reducing boundary errors.
  arXiv  Detail & Related papers  (2022-12-23T15:42:01Z)
• Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection [92.75961303269548]
  The ground plane prior is a very informative geometry clue in monocular 3D object detection (M3OD) We propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go. Our GPENet can outperform other methods and achieve state-of-the-art performance, well demonstrating the effectiveness and the superiority of the proposed approach.
  arXiv  Detail & Related papers  (2022-11-03T02:21:35Z)
• GSECnet: Ground Segmentation of Point Clouds for Edge Computing [7.481096704433562]
  GSECnet is an efficient ground segmentation framework designed to be deployable on a low-power edge computing unit. Our framework achieves the runtime inference of 135.2 Hz on a desktop platform.
  arXiv  Detail & Related papers  (2021-04-05T04:29:28Z)
• PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation [96.76882806139251]
  We propose a point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow. Rather than dense affinity learning, a sparse affinity map is generated upon selected points between the adjacent features. Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods.
  arXiv  Detail & Related papers  (2021-03-11T09:42:32Z)
• Few-shot 3D Point Cloud Semantic Segmentation [138.80825169240302]
  We propose a novel attention-aware multi-prototype transductive few-shot point cloud semantic segmentation method. Our proposed method shows significant and consistent improvements compared to baselines in different few-shot point cloud semantic segmentation settings.
  arXiv  Detail & Related papers  (2020-06-22T08:05:25Z)
• PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation [111.7241018610573]
  We present PointGroup, a new end-to-end bottom-up architecture for instance segmentation. We design a two-branch network to extract point features and predict semantic labels and offsets, for shifting each point towards its respective instance centroid. A clustering component is followed to utilize both the original and offset-shifted point coordinate sets, taking advantage of their complementary strength. We conduct extensive experiments on two challenging datasets, ScanNet v2 and S3DIS, on which our method achieves the highest performance, 63.6% and 64.0%, compared to 54.9% and 54.4% achieved by former best
  arXiv  Detail & Related papers  (2020-04-03T16:26:37Z)
• From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds [59.98665358527686]
  We propose a new method for segmentation-free joint estimation of orthogonal planes. Such unified scene exploration allows for multitudes of applications such as semantic plane detection or local and global scan alignment. Our experiments demonstrate the validity of our approach in numerous scenarios from wall detection to 6D tracking.
  arXiv  Detail & Related papers  (2020-01-21T06:51:47Z)

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.