A novel tree-structured point cloud dataset for skeletonization algorithm evaluation

• URL: http://arxiv.org/abs/2001.02823v1
• Date: Thu, 9 Jan 2020 03:35:57 GMT
• Title: A novel tree-structured point cloud dataset for skeletonization algorithm evaluation
• Authors: Yan Lin and Ji Liu and Jianlin Zhou
• Abstract summary: We construct a brand new tree-structured point cloud dataset, including ground truth skeletons, and point cloud models. Four types of point cloud are built on clean point cloud: point clouds with noise, point clouds with missing data, point clouds with different density, and point clouds with uneven density distribution. This dataset can be used as standard dataset for skeleton extraction algorithms.
• Score: 15.275934615615308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Curve skeleton extraction from unorganized point cloud is a fundamental task of computer vision and three-dimensional data preprocessing and visualization. A great amount of work has been done to extract skeleton from point cloud. but the lack of standard datasets of point cloud with ground truth skeleton makes it difficult to evaluate these algorithms. In this paper, we construct a brand new tree-structured point cloud dataset, including ground truth skeletons, and point cloud models. In addition, four types of point cloud are built on clean point cloud: point clouds with noise, point clouds with missing data, point clouds with different density, and point clouds with uneven density distribution. We first use tree editor to build the tree skeleton and corresponding mesh model. Since the implicit surface is sufficiently expressive to retain the edges and details of the complex branches model, we use the implicit surface to model the triangular mesh. With the implicit surface, virtual scanner is applied to the sampling of point cloud. Finally, considering the challenges in skeleton extraction, we introduce different methods to build four different types of point cloud models. This dataset can be used as standard dataset for skeleton extraction algorithms. And the evaluation between skeleton extraction algorithms can be performed by comparing the ground truth skeleton with the extracted skeleton.

Related papers

• SPEAL: Skeletal Prior Embedded Attention Learning for Cross-Source Point Cloud Registration [20.32815928095936]
  Point cloud registration has remained largely unexplored in cross-source point clouds and unstructured scenes. We propose a novel method termed SPEAL to leverage skeletal representations for effective learning of intrinsic topologies of point clouds. To the best of our knowledge, our approach is the first to facilitate point cloud registration with skeletal geometric priors.
  arXiv  Detail & Related papers  (2023-12-14T05:35:51Z)
• 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)
• Variational Relational Point Completion Network for Robust 3D Classification [59.80993960827833]
  Vari point cloud completion methods tend to generate global shape skeletons hence lack fine local details. This paper proposes a variational framework, point Completion Network (VRCNet) with two appealing properties. VRCNet shows great generalizability and robustness on real-world point cloud scans.
  arXiv  Detail & Related papers  (2023-04-18T17:03:20Z)
• PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning [72.07350827773442]
  We propose to solve open-set point cloud learning using a novel Point Cut-and-Mix mechanism. We use the Unknown-Point Simulator to simulate out-of-distribution data in the training stage. The Unknown-Point Estimator module learns to exploit the point cloud's feature context for discriminating the known and unknown data.
  arXiv  Detail & Related papers  (2022-12-05T03:53:51Z)
• Deep Geometry Post-Processing for Decompressed Point Clouds [32.72083309729585]
  Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission. We propose a novel learning-based post-processing method to enhance the decompressed point clouds. Experimental results show that the proposed method can significantly improve the quality of the decompressed point clouds.
  arXiv  Detail & Related papers  (2022-04-29T08:57:03Z)
• SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering [21.563862632172363]
  We propose a self-supervised point cloud upsampling network (SSPU-Net) to generate dense point clouds without using ground truth. To achieve this, we exploit the consistency between the input sparse point cloud and generated dense point cloud for the shapes and rendered images.
  arXiv  Detail & Related papers  (2021-08-01T13:26:01Z)
• VoxelContext-Net: An Octree based Framework for Point Cloud Compression [20.335998518653543]
  We propose a two-stage deep learning framework called VoxelContext-Net for both static and dynamic point cloud compression. We first extract the local voxel representation that encodes the spatial neighbouring context information for each node in the constructed octree. In the entropy coding stage, we propose a voxel context based deep entropy model to compress the symbols of non-leaf nodes.
  arXiv  Detail & Related papers  (2021-05-05T16:12:48Z)
• Refinement of Predicted Missing Parts Enhance Point Cloud Completion [62.997667081978825]
  Point cloud completion is the task of predicting complete geometry from partial observations using a point set representation for a 3D shape. Previous approaches propose neural networks to directly estimate the whole point cloud through encoder-decoder models fed by the incomplete point set. This paper proposes an end-to-end neural network architecture that focuses on computing the missing geometry and merging the known input and the predicted point cloud.
  arXiv  Detail & Related papers  (2020-10-08T22:01:23Z)
• Self-Sampling for Neural Point Cloud Consolidation [83.31236364265403]
  We introduce a novel technique for neural point cloud consolidation which learns from only the input point cloud. We repeatedly self-sample the input point cloud with global subsets that are used to train a deep neural network. We demonstrate the ability to consolidate point sets from a variety of shapes, while eliminating outliers and noise.
  arXiv  Detail & Related papers  (2020-08-14T17:16:02Z)
• Cascaded Refinement Network for Point Cloud Completion [74.80746431691938]
  We propose a cascaded refinement network together with a coarse-to-fine strategy to synthesize the detailed object shapes. Considering the local details of partial input with the global shape information together, we can preserve the existing details in the incomplete point set. We also design a patch discriminator that guarantees every local area has the same pattern with the ground truth to learn the complicated point distribution.
  arXiv  Detail & Related papers  (2020-04-07T13:03:29Z)

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.