scispace - formally typeset
Topic

Point (geometry)

About: Point (geometry) is a(n) research topic. Over the lifetime, 16807 publication(s) have been published within this topic receiving 186822 citation(s). The topic is also known as: fixed point (survey).

...read more

Papers
More filters

Journal ArticleDOI
Paul J. Besl1, H.D. McKay1Institutions (1)
Abstract: The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces. >

...read more

15,673 citations


Proceedings Article
Charles R. Qi1, Li Yi1, Hao Su2, Leonidas J. Guibas1Institutions (2)
07 Jun 2017-
Abstract: Few prior works study deep learning on point sets. PointNet is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds.

...read more

3,295 citations


01 Jan 1991-

2,399 citations


Posted Content
Charles R. Qi1, Li Yi1, Hao Su2, Leonidas J. Guibas1Institutions (2)
TL;DR: A hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set and proposes novel set learning layers to adaptively combine features from multiple scales to learn deep point set features efficiently and robustly.

...read more

Abstract: Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds.

...read more

2,215 citations


Journal ArticleDOI
TL;DR: This paper is a general description of spatstat and an introduction for new users.

...read more

Abstract: spatstat is a package for analyzing spatial point pattern data. Its functionality includes exploratory data analysis, model-fitting, and simulation. It is designed to handle realistic datasets, including inhomogeneous point patterns, spatial sampling regions of arbitrary shape, extra covariate data, and "marks" attached to the points of the point pattern. A unique feature of spatstat is its generic algorithm for fitting point process models to point pattern data. The interface to this algorithm is a function ppm that is strongly analogous to lm and glm. This paper is a general description of spatstat and an introduction for new users.

...read more

2,073 citations


Network Information
Related Topics (5)
Dupin indicatrix

17 papers, 178 citations

75% related
Viviani's curve

2 papers, 1 citations

72% related
Line (geometry)

13K papers, 143.6K citations

71% related
Feature recognition

2.4K papers, 28.1K citations

71% related
Additive Manufacturing File Format

5 papers, 101 citations

71% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202218
2021669
2020949
20191,173
20181,023
2017850

Top Attributes

Show by:

Topic's top 5 most impactful authors

Erdal Karapınar

14 papers, 81 citations

Xin-Yuan Wu

10 papers, 60 citations

Micha Sharir

10 papers, 181 citations

Oswin Aichholzer

8 papers, 185 citations

Mujahid Abbas

8 papers, 62 citations