A Benchmark for Point Clouds Registration Algorithms

• URL: http://arxiv.org/abs/2003.12841v3
• Date: Tue, 26 Apr 2022 12:23:52 GMT
• Title: A Benchmark for Point Clouds Registration Algorithms
• Authors: Simone Fontana, Daniele Cattaneo, Augusto Luis Ballardini, Matteo Vaghi and Domenico Giorgio Sorrenti
• Abstract summary: Point clouds registration is a fundamental step of many point clouds processing pipelines. Most algorithms are tested on data that are collected ad-hoc and not shared with the research community. This work aims at encouraging authors to use a public and shared benchmark, instead of data collected ad-hoc.
• Score: 6.667628085623009
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Point clouds registration is a fundamental step of many point clouds processing pipelines; however, most algorithms are tested on data that are collected ad-hoc and not shared with the research community. These data often cover only a very limited set of use cases; therefore, the results cannot be generalised. Public datasets proposed until now, taken individually, cover only a few kinds of environment and mostly a single sensor. For these reasons, we developed a benchmark, for localization and mapping applications, using multiple publicly available datasets. In this way, we are able to cover many kinds of environment and many kinds of sensor that can produce point clouds. Furthermore, the ground truth has been thoroughly inspected and evaluated to ensure its quality. For some of the datasets, the accuracy of the ground truth measuring system was not reported by the original authors, therefore we estimated it with our own novel method, based on an iterative registration algorithm. Along with the data, we provide a broad set of registration problems, chosen to cover different types of initial misalignment, various degrees of overlap, and different kinds of registration problems. Lastly, we propose a metric to measure the performances of registration algorithms: it combines the commonly used rotation and translation errors together, to allow an objective comparison of the alignments. This work aims at encouraging authors to use a public and shared benchmark, instead of data collected ad-hoc, to ensure objectivity and repeatability, two fundamental characteristics in any scientific field.

Related papers

• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• Fact Checking Beyond Training Set [64.88575826304024]
  We show that the retriever-reader suffers from performance deterioration when it is trained on labeled data from one domain and used in another domain. We propose an adversarial algorithm to make the retriever component robust against distribution shift. We then construct eight fact checking scenarios from these datasets, and compare our model to a set of strong baseline models.
  arXiv  Detail & Related papers  (2024-03-27T15:15:14Z)
• An Algorithm for Streaming Differentially Private Data [7.726042106665366]
  We derive an algorithm for differentially private synthetic streaming data generation, especially curated towards spatial datasets. The utility of our algorithm is verified on both real-world and simulated datasets.
  arXiv  Detail & Related papers  (2024-01-26T00:32:31Z)
• infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information [68.76707843019886]
  infoVerse is a universal framework for dataset characterization. infoVerse captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. In three real-world applications (data pruning, active learning, and data annotation), the samples chosen on infoVerse space consistently outperform strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T18:12:48Z)
• Private Domain Adaptation from a Public Source [48.83724068578305]
  We design differentially private discrepancy-based algorithms for adaptation from a source domain with public labeled data to a target domain with unlabeled private data. Our solutions are based on private variants of Frank-Wolfe and Mirror-Descent algorithms.
  arXiv  Detail & Related papers  (2022-08-12T06:52:55Z)
• Are we ready for beyond-application high-volume data? The Reeds robot perception benchmark dataset [3.781421673607643]
  This paper presents a dataset, called Reeds, for research on robot perception algorithms. The dataset aims to provide demanding benchmark opportunities for algorithms, rather than providing an environment for testing application-specific solutions.
  arXiv  Detail & Related papers  (2021-09-16T23:21:42Z)
• Learning multiview 3D point cloud registration [74.39499501822682]
  We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm. Our approach outperforms the state-of-the-art by a significant margin, while being end-to-end trainable and computationally less costly.
  arXiv  Detail & Related papers  (2020-01-15T03:42:14Z)
• NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization [101.13851473792334]
  We construct a large-scale congested crowd counting and localization dataset, NWPU-Crowd, consisting of 5,109 images, in a total of 2,133,375 annotated heads with points and boxes. Compared with other real-world datasets, it contains various illumination scenes and has the largest density range (020,033) We describe the data characteristics, evaluate the performance of some mainstream state-of-the-art (SOTA) methods, and analyze the new problems that arise on the new data.
  arXiv  Detail & Related papers  (2020-01-10T09:26:04Z)
• TCM-ICP: Transformation Compatibility Measure for Registering Multiple LIDAR Scans [4.5412347600435465]
  We present an algorithm for registering multiple, overlapping LiDAR scans. In this work, we introduce a geometric metric called Transformation Compatibility Measure (TCM) which aids in choosing the most similar point clouds for registration. We evaluate the proposed algorithm on four different real world scenes and experimental results shows that the registration performance of the proposed method is comparable or superior to the traditionally used registration methods.
  arXiv  Detail & Related papers  (2020-01-04T21:05:27Z)

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.