Point Cloud Library

Résumé

The Point Cloud Library (PCL) is an open-source library of algorithms for point cloud processing tasks and 3D geometry processing, such as occur in three-dimensional computer vision. The library contains algorithms for filtering, feature estimation, surface reconstruction, 3D registration, model fitting, object recognition, and . Each module is implemented as a smaller library that can be compiled separately (for example, libpcl_filters, libpcl_features, libpcl_surface, ...). PCL has its own data format for storing point clouds - PCD (Point Cloud Data), but also allows datasets to be loaded and saved in many other formats. It is written in C++ and released under the BSD license. These algorithms have been used, for example, for perception in robotics to filter outliers from noisy data, stitch 3D point clouds together, segment relevant parts of a scene, extract keypoints and compute descriptors to recognize objects in the world based on their geometric appearance, and create surfaces

Source officielle

https://en.wikipedia.org/wiki/Point_Cloud_Library

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PointXR: A toolbox for visualization and subjective evaluation of point clouds in virtual reality

Evangelos Alexiou, Touradj Ebrahimi, Nanyang Yang

In this study, we explore the use of virtual reality to subjectively evaluate the visual quality of point cloud contents. To this aim, we develop the PointXR toolbox, a set of Unity applications that can host experiments under variants of interactive and passive evaluation protocols. An auxiliary tool to facilitate the configuration of the supported rendering schemes for point cloud visualization is provided as part of it. Our toolbox is employed to conduct two validating experiments in a virtual environment with 6 degrees of freedom. The purpose is to assess the performance of color encoders that are incorporated in the upcoming MPEG standard on point cloud compression. For this study, we convert a set of mesh models to point cloud contents, and form a high- quality cultural heritage repository, namely, PointXR dataset. A comparison between the adopted protocols and the codecs’ performance is carried based on the ratings obtained from both experiments. Finally, interactivity patterns based on behavioral data that were recorded during the evaluations are extracted, and results are discussed. The PointXR toolbox, the PointXR dataset, and the experimental results are made publicly available.

2020

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Modern information technologies and human-centric communication systems employ advanced content representations for richer portrayals of the real world. The newly adopted imaging modalities offer additional information cues and permit the depiction of realistic sceneries, enabling immersive experiences and promoting the engagement of the user with the content. In this context, point clouds have emerged as an attractive option to represent immersive media. This type of visual data has seen a revived interest in the recent years, following the release of low-cost depth sensors and the wide integration of modern graphics processing units in mobile phones and personal computers. Point clouds can be naturally employed in extended reality applications that involve 6 degree-of-freedom interactions, allowing adjustments of the 3-D visual information in a per-point basis. At the same time, complexity reductions are promoted when compared to mesh modelling counterpart, due to the absence of connectivity information and the elimination of corresponding constraints from acquisition to rendering. Yet, the vast amount of information that is required for faithful content representations implies the necessity for efficient data structures and compression algorithms. In particular, new coding schemes must be designed in order to reduce the amount of data and by extension the costs in processing, storage, and transmission of point clouds, while lossy compression solutions should restrain degradations for more appealing results. Furthermore, adequate subjective quality evaluation methodologies tailored to the nature of this 3D imaging modality are essential in order to obtain ground-truth data, and to better understand the impact of compression and rendering artifacts in visual quality. The development of high-performing objective metrics is also fundamental to accurately predict the perceptual quality of degraded models. In this thesis, we address the aforementioned challenges by proposing new subjective quality assessment methodologies that better simulate realistic use-cases of 3D model consumption. We examine several aspects related to point cloud visualization and display means, by exploring different rendering approaches and by introducing experimental set-ups that offer different degrees of interactivity to the end-user. The behavior of human observers in 6 degrees-of-freedom virtual reality scenes is analysed, and visual attention maps are constructed using head and gaze trajectories recorded from eye-tracking experiments. Moreover, navigation data obtained from interactive subjective evaluations in desktop arrangements are exploited to improve image-based quality metrics, whose performance is examined in predicting visual impairments on point cloud contents. In the same line of research, we design novel point-based quality predictors for point cloud topology and texture degradations, and we rigorously analyse their performance using several subjectively annotated data sets. Furthermore, adopting well-established subjective evaluation methodologies, state-of-the-art compression solutions are benchmarked and best practices for rate allocation between geometry and texture encoding are derived. Lastly, a learning-based point cloud compression solution for encoding of both geometric and color information is proposed, and the impact of a series of parameters is examined on the obtained performance to pave the path for future efforts on the field.

EPFL2021

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SegMap: Segment-based mapping and localization using data-driven descriptors

Andrei Cramariuc, Roland Siegwart

Precisely estimating a robot’s pose in a prior, global map is a fundamental capability for mobile robotics, e.g., autonomous driving or exploration in disaster zones. This task, however, remains challenging in unstructured, dynamic environments, where local features are not discriminative enough and global scene descriptors only provide coarse information. We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds. Working at the level of segments offers increased invariance to view-point and local structural changes, and facilitates real-time processing of large-scale 3D data. SegMap exploits a single compact data-driven descriptor for performing multiple tasks: global localization, 3D dense map reconstruction, and semantic information extraction. The performance of SegMap is evaluated in multiple urban driving and search and rescue experiments. We show that the learned SegMap descriptor has superior segment retrieval capabilities, compared with state-of-the-art handcrafted descriptors. As a consequence, we achieve a higher localization accuracy and a 6% increase in recall over state-of-the-art handcrafted descriptors. These segment-based localizations allow us to reduce the open-loop odometry drift by up to 50%. SegMap is open-source available along with easy to run demonstrations.

2019

Chargement

EPFL2021