Automatic Labelling of Point Clouds Using Image Semantic Segmentation

Isesõitvaid autosid loetakse tehisintellekti järgmiseks suureks saavutuseks. Need kasutavad mitmesuguseid sensoreid, nt kaamera ja LiDAR, et koguda infot ümbritseva maailma kohta. LiDAR salvestab andmed punktipilvena, milles iga punkt on esitatud kolmemõõtmeliste koordinaatidega. Uusimad sügavad närvivõrgud suudavad käsitleda punktipilve algsel kujul, kuid märgendatud andmete kogumine treeningprotsessi jaoks on keeruline ning kulukas. Käesoleva töö eesmärk on kasutada semantiliselt segmenteeritud pilte 3D punktipilve märgendamiseks, võimaldades seeläbi koguda eelmainitud mudelite treenimiseks märgendatud andmeid odavamalt. Lisaks hindame olemasolevate semantilise segmenteerimise mudelite kasutamist suure koguse punktipilvede märgendamiseks automaatselt. Meetodi testimiseks kasutame KITTI andmestikku, sest see sisaldab nii kaamera kui ka LiDARi andmeid iga stseeni jaoks. Kaamera piltide pikseltasemel märgendamiseks kasutame DeepLabv3+ semantilise segmentatsiooni mudelit. Saadud märgendused projitseeritakse seejärel 3D punktipilvele, mille pealt treenitakse PointNet++ mudel. Viimane on seejärel võimeline punktipilvi segmenteerima ilma lisainfota. Eksperimentide tulemused näitavad, et PointNet++ suudab projitseeritud märgendustest võrdlemisi hästi õppida. Tulemuste võrdlused objektide teadaolevate asukohtadega on paljulubavad, saavutades kõrge täpsuse jalakäijate tuvastamisel ning keskmise täpsuse autode tuvastamisel.Autonomous driving is often seen as the next big breakthrough in artificial intelligence. Autonomous vehicles use a variety of sensors to obtain knowledge from the world, for example cameras and LiDARs. LiDAR provides 3D data about the surrounding world in the form of a point cloud. New deep learning models have emerged that allow for learning directly on point clouds, but obtaining labelled data for training these models is difficult and expensive. We propose to use semantically segmented camera images to project labels from 2D to 3D, therefore enabling the use of cheaper ground truth data to train the aforementioned models. Furthermore, we evaluate the use of mature 2D semantic segmentation models to automatically label vast amounts of point cloud data. This approach is tested on the KITTI dataset, as it provides corresponding camera and LiDAR data for each scene. The DeepLabv3+ semantic segmentation model is used to label the camera images with pixel-level labels, which are then projected onto the 3D point cloud and finally a PointNet++ model is trained to do segmentation from point clouds only. Experiments show that projected 2D labels can be learned reasonably well by PointNet++. Evaluating the results with 3D ground truth provided with KITTI dataset produced promising results, with accuracy being high for detecting pedestrians, but mediocre for cars

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Polü(1-[2-(metakrüüloüüloksü)-etüül]-3-metüülimidasoolium)-i jõuvälja parametriseerimine

http://www.ester.ee/record=b4683185*es