Skeleton Extraction from 3D Point Clouds by Decomposing the Object into Parts

• URL: http://arxiv.org/abs/1912.11932v1
• Date: Thu, 26 Dec 2019 20:52:57 GMT
• Title: Skeleton Extraction from 3D Point Clouds by Decomposing the Object into Parts
• Authors: Vijai Jayadevan, Edward Delp, and Zygmunt Pizlo
• Abstract summary: We propose to extract curve skeletons, from unorganized point clouds, by decomposing the object into its parts. We use translational symmetry, the fundamental property of GCs, to extract parts from point clouds. A part based approach also provides a natural and intuitive interface for user interaction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decomposing a point cloud into its components and extracting curve skeletons from point clouds are two related problems. Decomposition of a shape into its components is often obtained as a byproduct of skeleton extraction. In this work, we propose to extract curve skeletons, from unorganized point clouds, by decomposing the object into its parts, identifying part skeletons and then linking these part skeletons together to obtain the complete skeleton. We believe it is the most natural way to extract skeletons in the sense that this would be the way a human would approach the problem. Our parts are generalized cylinders (GCs). Since, the axis of a GC is an integral part of its definition, the parts have natural skeletal representations. We use translational symmetry, the fundamental property of GCs, to extract parts from point clouds. We demonstrate how this method can handle a large variety of shapes. We compare our method with state of the art methods and show how a part based approach can deal with some of the limitations of other methods. We present an improved version of an existing point set registration algorithm and demonstrate its utility in extracting parts from point clouds. We also show how this method can be used to extract skeletons from and identify parts of noisy point clouds. A part based approach also provides a natural and intuitive interface for user interaction. We demonstrate the ease with which mistakes, if any, can be fixed with minimal user interaction with the help of a graphical user interface.

Related papers

• Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly [36.16249417050003]
  We propose to decompose shapes using a library of 3D parts provided by the user. We show that this approach gives higher reconstruction accuracy and more desirable decompositions than existing approaches.
  arXiv  Detail & Related papers  (2023-03-03T15:11:36Z)
• UNIF: United Neural Implicit Functions for Clothed Human Reconstruction and Animation [53.2018423391591]
  We propose a part-based method for clothed human reconstruction and animation with raw scans and skeletons as the input. Our method learns to separate parts from body motions instead of part supervision, thus can be extended to clothed humans and other articulated objects.
  arXiv  Detail & Related papers  (2022-07-20T11:41:29Z)
• EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation [19.817166425038753]
  This paper tackles the problem of parts-aware point cloud generation. A simple modification of the Variational Auto-Encoder yields a joint model of the point cloud itself. In addition to the flexibility afforded by our disentangled representation, the inductive bias introduced by our joint modelling approach yields the state-of-the-art experimental results on the ShapeNet dataset.
  arXiv  Detail & Related papers  (2021-10-13T12:38:01Z)
• Unsupervised Pose-Aware Part Decomposition for 3D Articulated Objects [68.73163598790255]
  We propose PPD (unsupervised Pose-aware Part Decomposition) to address a novel setting that explicitly targets man-made articulated objects with mechanical joints. We show that category-common prior learning for both part shapes and poses facilitates the unsupervised learning of (1) part decomposition with non-primitive-based implicit representation, and (2) part pose as joint parameters under single-frame shape supervision.
  arXiv  Detail & Related papers  (2021-10-08T23:53:56Z)
• Discovering 3D Parts from Image Collections [98.16987919686709]
  We tackle the problem of 3D part discovery from only 2D image collections. Instead of relying on manually annotated parts for supervision, we propose a self-supervised approach. Our key insight is to learn a novel part shape prior that allows each part to fit an object shape faithfully while constrained to have simple geometry.
  arXiv  Detail & Related papers  (2021-07-28T20:29:16Z)
• Point2Skeleton: Learning Skeletal Representations from Point Clouds [36.62519847312199]
  We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds. We first predict a set of skeletal points by learning a geometric transformation, and then analyze the connectivity of the skeletal points to form skeletal mesh structures.
  arXiv  Detail & Related papers  (2020-12-01T03:04:09Z)
• Non-Rigid Puzzles [50.213265511586535]
  We present a non-rigid multi-part shape matching algorithm. We assume to be given a reference shape and its multiple parts undergoing a non-rigid deformation. Experimental results on synthetic as well as real scans demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2020-11-26T00:32:30Z)
• AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph [54.701098964773756]
  We aim to recover 3D objects with semantic parts and can be directly edited. Our work makes an attempt towards recovering two types of primitive-shaped objects, namely, generalized cuboids and generalized cylinders. Our algorithm can recover high quality 3D models and outperforms existing methods in both instance segmentation and 3D reconstruction.
  arXiv  Detail & Related papers  (2020-05-27T12:16:24Z)
• Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering [53.16864661460889]
  Recent works succeed in regression-based methods which estimate parametric models directly through a deep neural network supervised by 3D ground truth. In this paper, we introduce body segmentation as critical supervision. To improve the reconstruction with part segmentation, we propose a part-level differentiable part that enables part-based models to be supervised by part segmentation.
  arXiv  Detail & Related papers  (2020-03-24T14:25:46Z)
• A novel tree-structured point cloud dataset for skeletonization algorithm evaluation [15.275934615615308]
  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.
  arXiv  Detail & Related papers  (2020-01-09T03:35:57Z)

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.