Fitting and recognition of geometric primitives in segmented 3D point clouds using a localized voting procedure

• URL: http://arxiv.org/abs/2205.15426v1
• Date: Mon, 30 May 2022 20:47:43 GMT
• Title: Fitting and recognition of geometric primitives in segmented 3D point clouds using a localized voting procedure
• Authors: Andrea Raffo, Chiara Romanengo, Bianca Falcidieno, Silvia Biasotti
• Abstract summary: We introduce a novel technique for processing point clouds that, through a voting procedure, is able to provide an initial estimate of the primitive parameters each type. By using these estimates we localize the search of the optimal solution in a dimensionally-reduced space, making it efficient to extend the HT to more primitive than those that generally found in the literature.
• Score: 1.8352113484137629
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The automatic creation of geometric models from point clouds has numerous applications in CAD (e.g., reverse engineering, manufacturing, assembling) and, more in general, in shape modelling and processing. Given a segmented point cloud representing a man-made object, we propose a method for recognizing simple geometric primitives and their interrelationships. Our approach is based on the Hough transform (HT) for its ability to deal with noise, missing parts and outliers. In our method we introduce a novel technique for processing segmented point clouds that, through a voting procedure, is able to provide an initial estimate of the geometric parameters characterizing each primitive type. By using these estimates, we localize the search of the optimal solution in a dimensionally-reduced parameter space thus making it efficient to extend the HT to more primitives than those that are generally found in the literature, i.e. planes and spheres. Then, we extract a number of geometric descriptors that uniquely characterize a segment, and, on the basis of these descriptors, we show how to aggregate parts of primitives (segments). Experiments on both synthetic and industrial scans reveal the robustness of the primitive fitting method and its effectiveness for inferring relations among segments.

Related papers

• Generalized Few-Shot Point Cloud Segmentation Via Geometric Words [54.32239996417363]
  Few-shot point cloud segmentation algorithms learn to adapt to new classes at the sacrifice of segmentation accuracy for the base classes. We present the first attempt at a more practical paradigm of generalized few-shot point cloud segmentation. We propose the geometric words to represent geometric components shared between the base and novel classes, and incorporate them into a novel geometric-aware semantic representation.
  arXiv  Detail & Related papers  (2023-09-20T11:24:33Z)
• Marching-Primitives: Shape Abstraction from Signed Distance Function [29.7543198254021]
  We present a novel method, named Marching-Primitives, to obtain a primitive-based abstraction directly from an SDF. Our method grows geometric primitives iteratively by analyzing the connectivity of voxels. We evaluate the performance of our method on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-03-23T11:42:35Z)
• Recognising geometric primitives in 3D point clouds of mechanical CAD objects [1.8352113484137629]
  The problem faced in this paper concerns the recognition of simple and complex geometric primitives in point clouds. A large number of points, the presence of noise, outliers, missing or redundant parts and uneven distribution are the main problems to be addressed to meet this need. We propose a solution, based on the Hough transform, that can recognize simple and complex geometric primitives and is robust to noise, outliers, and missing parts.
  arXiv  Detail & Related papers  (2023-01-11T09:33:55Z)
• Zero-shot point cloud segmentation by transferring geometric primitives [68.18710039217336]
  We investigate zero-shot point cloud semantic segmentation, where the network is trained on seen objects and able to segment unseen objects. We propose a novel framework to learn the geometric primitives shared in seen and unseen categories' objects and employ a fine-grained alignment between language and the learned geometric primitives.
  arXiv  Detail & Related papers  (2022-10-18T15:06:54Z)
• Primitive-based Shape Abstraction via Nonparametric Bayesian Inference [29.7543198254021]
  We propose a novel non-parametric Bayesian statistical method to infer an abstraction, consisting of an unknown number of geometric primitives, from a point cloud. Our method outperforms the state-of-the-art in terms of accuracy and is generalizable to various types of objects.
  arXiv  Detail & Related papers  (2022-03-28T13:00:06Z)
• Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks [118.20778308823779]
  We present a novel 3D primitive representation that defines primitives using an Invertible Neural Network (INN) Our model learns to parse 3D objects into semantically consistent part arrangements without any part-level supervision.
  arXiv  Detail & Related papers  (2021-03-18T17:59:31Z)
• Deep Magnification-Flexible Upsampling over 3D Point Clouds [103.09504572409449]
  We propose a novel end-to-end learning-based framework to generate dense point clouds. We first formulate the problem explicitly, which boils down to determining the weights and high-order approximation errors. Then, we design a lightweight neural network to adaptively learn unified and sorted weights as well as the high-order refinements.
  arXiv  Detail & Related papers  (2020-11-25T14:00:18Z)
• Primal-Dual Mesh Convolutional Neural Networks [62.165239866312334]
  We propose a primal-dual framework drawn from the graph-neural-network literature to triangle meshes. Our method takes features for both edges and faces of a 3D mesh as input and dynamically aggregates them. We provide theoretical insights of our approach using tools from the mesh-simplification literature.
  arXiv  Detail & Related papers  (2020-10-23T14:49:02Z)
• SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform [49.51977253452456]
  We present an efficient method for 3D shape segmentation based on the medial axis transform (MAT) of the input shape. Specifically, with the rich geometrical and structural information encoded in the MAT, we are able to identify the various types of junctions between different parts of a 3D shape. Our method outperforms the state-of-the-art methods in terms of segmentation quality and is also one order of magnitude faster.
  arXiv  Detail & Related papers  (2020-10-22T07:15:23Z)
• Geometric Attention for Prediction of Differential Properties in 3D Point Clouds [32.68259334785767]
  In this study, we present a geometric attention mechanism that can provide such properties in a learnable fashion. We establish the usefulness of the proposed technique with several experiments on the prediction of normal vectors and the extraction of feature lines.
  arXiv  Detail & Related papers  (2020-07-06T07:40:26Z)

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.