A Review of Panoptic Segmentation for Mobile Mapping Point Clouds

3D point cloud panoptic segmentation is the combined task to (i) assign each point to a semantic class and (ii) separate the points in each class into object instances. Recently there has been an increased interest in such comprehensive 3D scene understanding, building on the rapid advances of semantic segmentation due to the advent of deep 3D neural networks. Yet, to date there is very little work about panoptic segmentation of outdoor mobile-mapping data, and no systematic comparisons. The present paper tries to close that gap. It reviews the building blocks needed to assemble a panoptic segmentation pipeline and the related literature. Moreover, a modular pipeline is set up to perform comprehensive, systematic experiments to assess the state of panoptic segmentation in the context of street mapping. As a byproduct, we also provide the first public dataset for that task, by extending the NPM3D dataset to include instance labels. That dataset and our source code are publicly available. We discuss which adaptations are need to adapt current panoptic segmentation methods to outdoor scenes and large objects. Our study finds that for mobile mapping data, KPConv performs best but is slower, while PointNet++ is fastest but performs significantly worse. Sparse CNNs are in between. Regardless of the backbone, Instance segmentation by clustering embedding features is better than using shifted coordinates

3D-aware Image Generation using 2D Diffusion Models

In this paper, we introduce a novel 3D-aware image generation method that leverages 2D diffusion models. We formulate the 3D-aware image generation task as multiview 2D image set generation, and further to a sequential unconditional-conditional multiview image generation process. This allows us to utilize 2D diffusion models to boost the generative modeling power of the method. Additionally, we incorporate depth information from monocular depth estimators to construct the training data for the conditional diffusion model using only still images. We train our method on a large-scale dataset, i.e., ImageNet, which is not addressed by previous methods. It produces high-quality images that significantly outperform prior methods. Furthermore, our approach showcases its capability to generate instances with large view angles, even though the training images are diverse and unaligned, gathered from "in-the-wild" real-world environments.Comment: Website: https://jeffreyxiang.github.io/ivid

A theoretical investigation on the parametric instability excited by X-mode polarized electromagnetic wave at Tromsø

Recent ionospheric modification experiments performed at Tromsø, Norway, have indicated that X-mode pump wave is capable of stimulating high-frequency enhanced plasma lines, which manifests the excitation of parametric instability. This paper investigates theoretically how the observation can be explained by the excitation of parametric instability driven by X-mode pump wave. The threshold of the parametric instability has been calculated for several recent experimental observations at Tromsø, illustrating that our derived equations for the excitation of parametric instability for X-mode heating can explain the experimental observations. According to our theoretical calculation, a minimum fraction of pump wave electric field needs to be directed along the geomagnetic field direction in order for the parametric instability threshold to be met. A full-wave finite difference time domain simulation has been performed to demonstrate that a small parallel component of pump wave electric field can be achieved during X-mode heating in the presence of inhomogeneous plasma

Surfactant‐directed Pd‐nanoparticle assemblies as efficient nanoreactors for water remediation

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Towards accurate instance segmentation in large-scale LiDAR point clouds

Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene understanding, from city mapping to forest management. Existing methods struggle to segment nearby instances of the same semantic category, like adjacent pieces of street furniture or neighbouring trees, which limits their usability for inventory- or management-type applications that rely on object instances. This study explores the steps of the panoptic segmentation pipeline concerned with clustering points into object instances, with the goal to alleviate that bottleneck. We find that a carefully designed clustering strategy, which leverages multiple types of learned point embeddings, significantly improves instance segmentation. Experiments on the NPM3D urban mobile mapping dataset and the FOR-instance forest dataset demonstrate the effectiveness and versatility of the proposed strategy

Humidity Influence on Mechanics and Failure of Paper Materials: Joint Numerical and Experimental Study on Fiber and Fiber Network Scale

Paper materials are natural composite materials and well-known to be hydrophilic unless chemical and mechanical processing treatments are undertaken. The relative humidity impacts the fiber elasticity, the fiber-fiber bonds and the failure mechanism. In this work, we present a comprehensive experimental and computational study on the mechanical and failure behaviour of the fiber and the fiber network under humidity influence. The manually extracted cellulose fiber is exposed to different levels of humidity, and then mechanically characterized using Atomic Force Microscopy, which delivers the humidity dependent longitudinal Young's modulus. The obtained relationship allows calculation of fiber elastic modulus at any humidity level. Moreover, by using Confoncal Laser Scanning Microscopy, the coefficient of hygroscopic expansion of the fibers is determined. On the other hand, we present a finite element model to simulate the deformation and the failure of the fiber network. The model includes the fiber anisotropy and the hygroscopic expansion using the experimentally determined constants. In addition, it regards the fiber-fiber bonding and damage by using a humidity dependent cohesive zone interface model. Finite element simulations on exemplary fiber network samples are performed to demonstrate the influence of different aspects including relative humidity and fiber-fiber bonding parameters on the mechanical features such as force-elongation curves, wet strength, extensiability and the local fiber-fiber debonding. In meantime, fiber network failure in a locally wetted region is revealed by tracking of individually stained fibers using in-situ imaging techniques. Both the experimental data and the cohesive finite element simulations demonstrate the pull-out of fibers and imply the significant role of the fiber-fiber debonding in the failure process of the wet paper.Comment: 21 pages,10 figure

Multiscale simulation of grain refinement induced by dynamic recrystallization of Ti6Al4V alloy during high speed machining

During high speed machining (HSM), the strong thermal-mechanical coupling can lead to the microstructure evolution in the deformation zone of workpiece. Grain refinement may occur, which has great effects on the mechanical behavior, and even on the fatigue strength and corrosion resistance of the machined surface. The development of multiscale models to predict the microstructure evolution is gaining rising interest. This study aims to investigate the grain refinement induced by dynamic recrystallization (DRX) occurring in HSM of Ti6Al4V, through finite element (FE) and cellular automata (CA) methods. An orthogonal cutting model for HSM of Ti6Al4V is developed combining a modified Johnson-Cook constitutive model (TANH) and Johnson-Mehl- Avrami-Kolmogorov (JMAK) DRX model. The CA model is proposed considering dislocation density evolution, grain nucleation and growth. The 2D mesoscopic microstructure evolution is simulated successfully by the CA model in which the input deformation parameters come from the FE simulations of the orthogonal cutting process. Finally, the grain size and microstructure morphology calculated by both FE and CA methods are compared with those characteristics obtained experimentally by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Identical microstructure predictions from both CA and FE methods show a reasonable agreement with the TEM results, on the condition that twinning and phase transformation are not considered in the simulations. This work proves that the combination of FE and CA methods is an effective approach to achieve a more comprehensive understanding of the microstructure evolution and its effect on mechanical behavior during HSM. It shows that the rise of both DRX volume fraction and DRX grain size finally results in the slightly decreasing of average grain size of serrated chips with the increase of cutting speed, which leads to the strain softening phenomenon of flow stress