Cloud Geometry Research Articles

Effects of Cloud Geometry and Metallicity on Shattering and Coagulation of Cold Gas, and Implications for Cold Streams Penetrating Virial Shocks

Abstract Theory and observations reveal that the circumgalactic medium (CGM) and the cosmic web at high redshifts are multiphase, with small clouds of cold gas embedded in a hot, diffuse medium. We study the ‘shattering’ of large, thermally unstable clouds into tiny cloudlets of size ℓshatter ∼ min(cstcool) using idealized numerical simulations. We expand upon previous works by exploring the effects of cloud geometry (spheres, streams, and sheets), metallicity, and an ionizing UV background. We find that ‘shattering’ is mainly triggered by clouds losing sonic contact and rapidly imploding, leading to a reflected shock that causes the cloud to re-expand and induces Richtmyer-Meshkov instabilities at its interface. The fragmented cloudlets experience a drag force from the surrounding hot gas, leading to recoagulation into larger clouds. We distinguish between ‘fast’ and ‘slow’ coagulation regimes. Sheets are always in the ‘fast’ coagulation regime while streams and spheres transition to ’slow’ coagulation above a critical overdensity, which is smallest for spheres. Surprisingly, ℓshatter does not appear to be a characteristic clump size even if it is well resolved. Rather, fragmentation continues until the grid scale with a mass distribution of N( > m)∝m−1. We apply our results to cold streams feeding massive ($M_{\rm v}\lower.5ex\hbox{$\; \buildrel> \over \sim \;$}10^{12}\, {\rm M}_\odot$) galaxies at $z\lower.5ex\hbox{$\; \buildrel> \over \sim \;$}2$ from the cosmic web, finding that streams likely shatter upon entering the hot CGM through the virial shock. This could explain the large clumping factors and covering fractions of cold gas around such galaxies, and may be related to galaxy quenching by preventing cold streams from reaching the central galaxy.

Consideration of the cloud motion for aircraft-based stereographically derived cloud geometry and cloud top heights

Abstract. Cloud geometry and in particular cloud top heights can be derived from 2-D camera measurements by applying a stereographic method to data from an overflight over a scene of clouds (see, e.g., Kölling et al., 2019). Although airplane overpasses are relatively fast, cloud motion with the wind is important and can result in errors in the cloud localization. Here, the impact of the wind is investigated using the method from Kölling et al. (2019) for measurements of the airborne hyperspectral imaging system spectrometer of the Munich Aerosol Cloud Scanner (specMACS). Further, a method for cloud motion correction using model winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) is presented. It is shown that the update is important as the original algorithm without the cloud motion correction can over- or underestimate the cloud top heights by several hundred meters, depending on the wind speed and the relative wind direction. This is validated using data from the Elucidating the role of clouds–circulation coupling in climate (EUREC4A) campaign and realistic 3-D radiative transfer simulations. From the comparison of the derived cloud top heights with the expected ones from the model input, an average accuracy of the cloud top heights of less than (20±140) m (mean deviation and 1 standard deviation) is estimated for the updated method.

Multi-Grained Point Cloud Geometry Compression via Dual-Model Prediction with Extended Octree

The state-of-the-art geometry-based point cloud compression (G-PCC) (Octree) is the fine-grained approach, which uses the octree to partition point clouds into voxels and predicts them based on neighbor occupancy in narrower spaces. However, G-PCC (Octree) is less effective at compressing dense point clouds than multi-grained approaches (such as G-PCC (Trisoup)), which exploit the continuous point distribution in nodes partitioned by the pruned octree over larger spaces. Therefore, we propose a lossy multi-grained compression with extended octree and dual-model prediction. The extended octree, where each partitioned node contains intra-block and extra-block points, is applied to address poor prediction (such as overfitting) at the node edges of the octree partition. For the points of each multi-grained node, dual-model prediction fits surfaces and projects residuals onto the surfaces, reducing projection residuals for efficient 2D compression and fitting complexity. In addition, a hybrid DWT-DCT transform for 2D projection residuals mitigates the resolution degradation of DWT and the blocking effect of DCT during high compression. Experimental results demonstrate the superior performance of our method over advanced G-PCC (Octree), achieving BD-Rate gains of 55.9% and 45.3% for point-to-point ( D1 ) and point-to-plane ( D2 ) distortions, respectively. Our approach also outperforms G-PCC (Octree) and G-PCC (Trisoup) in subjective evaluation.

Enhancing Context Models for Point Cloud Geometry Compression With Context Feature Residuals and Multi-Loss

Enhancing Context Models for Point Cloud Geometry Compression With Context Feature Residuals and Multi-Loss

Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression.

The substantial data volume within dynamic point clouds representing three-dimensional moving entities necessitates advancements in compression techniques. Motion estimation (ME) is crucial for reducing point cloud temporal redundancy. Standard block-based ME schemes, which typically utilize the previously decoded point clouds as inter-reference frames, often yield inaccurate and translation-only estimates for dynamic point clouds. To overcome this limitation, we propose an advanced patch-based affine ME scheme for dynamic point cloud geometry compression. Our approach employs a forward-backward jointing ME strategy, generating affine motion-compensated frames for improved inter-geometry references. Before the forward ME process, point cloud motion analysis is conducted on previous frames to perceive motion characteristics. Then, a point cloud is segmented into deformable patches based on geometry correlation and motion coherence. During the forward ME process, affine motion models are introduced to depict the deformable patch motions from the reference to the current frame. Later, affine motion-compensated frames are exploited in the backward ME process to obtain refined motions for better coding performance. Experimental results demonstrate the superiority of our proposed scheme, achieving an average 6.28% geometry bitrate gain over the inter codec anchor. Additional results also validate the effectiveness of key modules within the proposed ME scheme.

Semi-automated approach towards efficient HD Maps generation and verification with Lanelet2 formats

Abstract. HD Maps (High-Definition Maps) serve as crucial resources for the domain of autonomous vehicle. Because HD Maps can provide detailed and accurate road information, the generation of HD Maps has been a labour-intensive and high cost. This research presents an innovative and semi-automated approach for efficient HD Maps generation by using assure mapping tool with deep learning techniques and mobile laser scanned point cloud geometry. The proposed method starts with data collection from various sources such as images, LiDAR point clouds, and integrated INS/GNSS trajectory data. These data are labelled by using a pre-trained model. After finishing post-labelling, these data are subjected to deep learning training by using VoxelNet and Yolact++ framework and leading to the generation of an AI model. The tool effectively recognizes and categorizes features such as road surface markings, traffic signs, and traffic lights, which can be further expanded as per requirements. Finally, the output format can be converted to OpenDRIVE, Lanelet2, and other else. Hence, the extracted lane lines can compare to the manual mapping data for verifying the accuracy. This study demonstrates that the proposed approach can be instrumental in streamlining the HD Maps generation procedure, reducing manual labour, and enhancing efficiency. The assure mapping tool proves to be an effective instrument, particularly when powered by deep learning algorithms and point cloud geometries, in the creation of reliable, comprehensive, and application-ready HD Maps.

A Weakly Supervised Vehicle Detection Method from LiDAR Point Clouds

Abstract. Training LiDAR point clouds object detectors requires a significant amount of annotated data, which is time-consuming and effort-demanding. Although weakly supervised 3D LiDAR-based methods have been proposed to reduce the annotation cost, their performance could be further improved. In this work, we propose a weakly supervised LiDAR-based point clouds vehicle detector that does not require any labels for the proposal generation stage and needs only a few labels for the refinement stage. It comprises two primary modules. The first is an unsupervised proposal generation module based on the geometry of point clouds. The second is the pseudo-label refinement module. We validate our method on two point clouds based object detection datasets, namely KITTI and ONCE, and compare it with various existing weakly supervised point clouds object detection methods. The experimental results demonstrate the method’s effectiveness with a small amount of labeled LiDAR point clouds.

Model-based evaluation of cloud geometry and droplet size retrievals from two-dimensional polarized measurements of specMACS

Abstract. Cloud radiative properties play a significant role in radiation and energy budgets and are influenced by both the cloud top height and the particle size distribution. Both cloud top heights and particle size distributions can be derived from 2-D intensity and polarization measurements by the airborne spectrometer of the Munich Aerosol Cloud Scanner (specMACS). The cloud top heights are determined using a stereographic method (Kölling et al., 2019), and the particle size distributions are derived in terms of the cloud effective radius and the effective variance from multidirectional polarized measurements of the cloudbow (Pörtge et al., 2023). In this study, the accuracy of the two methods is evaluated using realistic 3-D radiative transfer simulations of specMACS measurements of a synthetic field of shallow cumulus clouds, and possible error sources are determined. The simulations are performed with the 3-D Monte Carlo radiative transport model MYSTIC (Mayer, 2009) using cloud data from highly resolved large-eddy simulations (LESs). Both retrieval methods are applied to the simulated data and compared to the respective properties of the underlying cloud field from the LESs. Moreover, the influence of the cloud development on both methods is evaluated by applying the algorithms to idealized simulated data where the clouds did not change during the simulated overflight of 1 min over the cloud field. For the cloud top height retrieval, an absolute mean difference of less than 70 m with a standard deviation of about 130 m compared to the expected heights from the model is found. The elimination of the cloud development as a possible error source results in mean differences of (46±140) m. For the effective radius, an absolute average difference of about (-0.2±1.30) µm from the expected effective radius from the LES model input is derived for the realistic simulation and (-0.03±1.28) µm for the simulation without cloud development. The difference between the effective variance derived from the cloudbow retrieval and the expected effective variance is (0.02±0.05) for both simulations. Additional studies concerning the correlations between larger errors in the effective radius or variance and the optical thickness of the observed clouds have revealed that low values in the optical thickness do not have an impact on the accuracy of the retrieval.

From dense point clouds to semantic digital models: End-to-end AI-based automation procedure for Manhattan-world structures

The paper presents a novel method for automatically creating semantic digital models for buildings in Manhattan environments from dense point cloud data. Unlike previous approaches, which rely solely on data-driven methods, our method integrates artificial intelligence with domain engineering knowledge to overcome challenges in indoor point cloud processing and geometry representation in complex layouts. A feature-based Decision Tree classifier extracts main building elements, utilized in a knowledge-based algorithm for 3D space parsing. On this basis, an optimization process generates parameterized floor plans, used to finally create volumetric digital models. The method was validated on datasets from the Technical University of Munich and Stanford University, achieving a mean accuracy of approximately 0.08 m for model placement and 0.06 m for estimating element parameters, which highlights its effectiveness for generating a building's semantic digital model. This approach underscores the potential of AI integration in digital twinning workflows for more automated solutions.

Point cloud geometry compression with sparse cascaded residuals and sparse attention

AbstractEffective compression of point clouds is essential for implementing virtual and mixed reality applications, which require encoding millions or even tens of millions of points. This paper offers a new geometric compression for point clouds based on sparse cascaded residuals and sparse attention. A sparse cascaded residual module is posited to connect multiple residual modules through shortcuts, thereby augmenting the network's learning capacity and compression efficacy. The authors developed a sparse attention module to acquire global features by computing interdependencies among points, enhancing compression performance to a greater extent. Trade‐off parameters are employed to optimize the rate and distortion. The authors’ method outperforms the state‐of‐the‐art open‐source method regarding rate‐distortion on the ShapeNet, ModelNet, and Microsoft Voxelized Upper Bodies datasets, with average bjøntegaard‐delta (BD)‐rate gains of −14.44% and −15.38%.

Determining the umbrella cloud geometry of unwitnessed silicic explosive eruptions: A case study from Mount Mazama (Oregon, United States)

Volcanic Ash Transport and Dispersal Models (VATDMs) make real-time forecasts of tephra fall resulting from explosive eruptions possible. However, these predictions still mainly rely on eruption source parameters, such as erupted mass, total grain-size distribution, and plume height, gathered via thorough studies of past eruptions similar in nature. This dependency of eruption source parameters to analogous eruptions becomes particularly challenging when there are limited instances of similar events. An example is rhyodacitic to rhyolitic eruptions. This type of volcanic eruption has only been witnessed twice, at Chaitén (2008–2009) and Cordón Caulle (2011−2012), both in Chile. Here, we examine the 7.7 ka Cleetwood eruption of Mount Mazama (Oregon, USA), as a case study. This rhyodacitic eruption started explosively with two initial VEI 4, subplinian phases, and ended effusively with the emplacement of a rhyodacitic flow. We use the results of a detailed study of the proximal and medial tephra deposits as input in a VATDM to investigate the geometry and dimensions of the main plume formed during the Cleetwood eruption. We 1) constrain the erupted mass and calculate a detailed total grain-size distribution, 2) explore the Reanalysis 2 wind database to determine the direction and velocity of the local wind at the time of the eruption, and 3) use the VATDM Tephra2 with a grid-search method to estimate plume height, mass distribution within the plume, and the characteristics of tephra diffusion. We find that a vertical release of the erupted mass along a single line above the vent adequately replicates the measured mass loads but fails to simultaneously fit measured grain-size distributions at the same locations. We thus devise a method that not only accounts for a customized total grain-size distribution, real 1D wind patterns, and variable mass distribution within the plume, but also allows for adjustments to the size and location of an elliptical umbrella cloud. Using this method, we successfully replicate both local mass loads and high-resolution grain-size distributions and show that particles ≥0.125 mm from the lower Cleetwood unit were likely deposited from a 5 × 45 km2 umbrella reaching 16 km a.s.l., elongated in the direction of main wind intensity. This research contributes to enhancing the accuracy of predicting tephra transport from silicic volcanic eruptions. Moreover, it underscores the importance of utilizing grain-size data in combination with mass loads at specific locations to gain insights into the characteristics of the eruption plume, especially for eruptions that have not been directly observed.

A Density-Aware Point Cloud Geometry Compression Leveraging Cluster- Centric Processing

A Density-Aware Point Cloud Geometry Compression Leveraging Cluster- Centric Processing

Enhancing Octree-Based Context Models for Point Cloud Geometry Compression With Attention-Based Child Node Number Prediction

Enhancing Octree-Based Context Models for Point Cloud Geometry Compression With Attention-Based Child Node Number Prediction

Hierarchical Prior-Based Super Resolution for Point Cloud Geometry Compression.

The Geometry-based Point Cloud Compression (G-PCC) has been developed by the Moving Picture Experts Group to compress point clouds efficiently. Nevertheless, in its lossy mode, the reconstructed point cloud by G-PCC often suffers from noticeable distortions due to naïve geometry quantization (i.e., grid downsampling). This paper proposes a hierarchical prior-based super resolution method for point cloud geometry compression. The content-dependent hierarchical prior is constructed at the encoder side, which enables coarse-to-fine super resolution of the point cloud geometry at the decoder side. A more accurate prior generally yields improved reconstruction performance, albeit at the cost of increased bits required to encode this piece of side information. Our experiments on the MPEG Cat1A dataset demonstrate substantial Bjøntegaard-delta bitrate savings, surpassing the performance of the octree-based and trisoup-based G-PCC v14. We provide our implementations for reproducible research at https://github.com/lidq92/mpeg-pcc-tmc13.

ANFPCGC++: Point Cloud Geometry Coding Using Augmented Normalizing Flows and Transformer-Based Entropy Model

ANFPCGC++: Point Cloud Geometry Coding Using Augmented Normalizing Flows and Transformer-Based Entropy Model

Inter-Frame Compression for Dynamic Point Cloud Geometry Coding.

Efficient point cloud compression is essential for applications like virtual and mixed reality, autonomous driving, and cultural heritage. This paper proposes a deep learning-based inter-frame encoding scheme for dynamic point cloud geometry compression. We propose a lossy geometry compression scheme that predicts the latent representation of the current frame using the previous frame by employing a novel feature space inter-prediction network. The proposed network utilizes sparse convolutions with hierarchical multiscale 3D feature learning to encode the current frame using the previous frame. The proposed method introduces a novel predictor network for motion compensation in the feature domain to map the latent representation of the previous frame to the coordinates of the current frame to predict the current frame's feature embedding. The framework transmits the residual of the predicted features and the actual features by compressing them using a learned probabilistic factorized entropy model. At the receiver, the decoder hierarchically reconstructs the current frame by progressively rescaling the feature embedding. The proposed framework is compared to the state-of-the-art Video-based Point Cloud Compression (V-PCC) and Geometry-based Point Cloud Compression (G-PCC) schemes standardized by the Moving Picture Experts Group (MPEG). The proposed method achieves more than 88% BD-Rate (Bjøntegaard Delta Rate) reduction against G-PCCv20 Octree, more than 56% BD-Rate savings against G-PCCv20 Trisoup, more than 62% BD-Rate reduction against V-PCC intra-frame encoding mode, and more than 52% BD-Rate savings against V-PCC P-frame-based inter-frame encoding mode using HEVC. These significant performance gains are cross-checked and verified in the MPEG working group.

ASSESSING THE ALIGNMENT BETWEEN GEOMETRY AND COLORS IN TLS COLORED POINT CLOUDS

Abstract. The integration of the color information from RGB cameras with the point cloud geometry is used in numerous applications. However, little attention has been paid on errors that occur when aligning colors to points in terrestrial laser scanning (TLS) point clouds. Such errors may impact the performance of algorithms that utilize colored point clouds. Herein, we propose a procedure for assessing the alignment between the TLS point cloud geometry and colors. The procedure is based upon identifying artificial targets observed in both LiDAR-based point cloud intensity data and camera-based RGB data, and quantifying the quality of the alignment using differences between the target center coordinates estimated separately from these two data sources. Experimental results with eight scanners show that the quality of the alignment depends on the scanner, the software used for colorizing the point clouds, and may change with changing environmental conditions. While we found the effects of misalignment to be negligible for some scanners, they exhibited clearly systematic patterns exceeding the beam divergence, image and scan resolution for four of the scanners. The maximum deviations were about 2 mrad perpendicular to the line-of-sight when colorizing the point clouds with the respective manufacturer’s software or scanner in-built functions, while they were up to about 5 mrad when using a different software. Testing the alignment quality, e.g., using the approach presented herein, is thus important for applications requiring accurate alignment of the RGB colors with the point cloud geometry.

TransPCGC: Point Cloud Geometry Compression Based on Transformers

Due to the often substantial size of the real-world point cloud data, efficient transmission and storage have become critical concerns. Point cloud compression plays a decisive role in addressing these challenges. Recognizing the importance of capturing global information within point cloud data for effective compression, many existing point cloud compression methods overlook this crucial aspect. To tackle this oversight, we propose an innovative end-to-end point cloud compression method designed to extract both global and local information. Our method includes a novel Transformer module to extract rich features from the point cloud. Utilization of a pooling operation that requires no learnable parameters as a token mixer for computing long-distance dependencies ensures global feature extraction while significantly reducing both computations and parameters. Furthermore, we employ convolutional layers for feature extraction. These layers not only preserve the spatial structure of the point cloud, but also offer the advantage of parameter independence from the input point cloud size, resulting in a substantial reduction in parameters. Our experimental results demonstrate the effectiveness of the proposed TransPCGC network. It achieves average Bjontegaard Delta Rate (BD-Rate) gains of 85.79% and 80.24% compared to Geometry-based Point Cloud Compression (G-PCC). Additionally, in comparison to the Learned-PCGC network, our approach attains an average BD-Rate gain of 18.26% and 13.83%. Moreover, it is accompanied by a 16% reduction in encoding and decoding time, along with a 50% reduction in model size.

An end-to-end dynamic point cloud geometry compression in latent space

Dynamic point clouds are widely used for 3D data representation in various applications such as immersive and mixed reality, robotics and autonomous driving. However, their irregularity and large scale make efficient compression and transmission a challenge. Existing methods require high bitrates to encode point clouds since temporal correlation is not well considered. This paper proposes an end-to-end dynamic point cloud compression network that operates in latent space, resulting in more accurate motion estimation and more effective motion compensation. Specifically, a multi-scale motion estimation network is introduced to obtain accurate motion vectors. Motion information computed at a coarser level is upsampled and warped to the finer level based on cost volume analysis for motion compensation. Additionally, a residual compression network is designed to mitigate the effects of noise and inaccurate predictions by encoding latent residuals, resulting in smaller conditional entropy and better results. The proposed method achieves an average 12.09% and 14.76% (D2) BD-Rate gain over state-of-the-art Deep Dynamic Point Cloud Compression (D-DPCC) in experimental results. Compared to V-PCC, our framework showed an average improvement of 81.29% (D1) and 77.57% (D2).

Distinguishing and Matching-Aware Unsupervised Point Cloud Completion

Real-scanned point clouds are often incomplete due to occlusion, light reflection and limitations of sensor resolution, which impedes the related progress of downstream tasks, e.g., shape classification and object detection. Although there has been impressive research progress on the point cloud completion topic, they rely on the premise of extensive paired training data. However, collecting complete point clouds in some specified scenarios is labor-intensive and even impractical. To mitigate this problem, we propose DMNet, a distinguishing and matching-aware unsupervised point cloud completion network. Our work belongs to the group of unsupervised completion methods but goes beyond previous studies. Firstly, we propose a distinguishing-aware feature extractor to learn discriminable semantic information for different instances, simultaneously enhancing the robust invariant representation under noise disturbances. Secondly, we design a hierarchy-aware hyperbolic decoder to recover the complete geometry of point clouds, which not only can capture the implicit hierarchical relationships in data but also has an explicit extended nature. Finally, we develop a matching-aware refiner to eliminate noise points via aligning the topology structure of the input and predicted partial point clouds. Extensive experiments on MVP, Completion3D and KITTI datasets prove the effectiveness of our method, which performs favorably over state-of-the-art methods both quantitatively and qualitatively.