Cloud Holes Research Articles

Prior-guided restoration of intense local specular highlight in fringe projection profilometry images

This paper presents a novel prior-guided restoration method, to our knowledge, aimed at removing and recovering intense local specular highlight in fringe projection profilometry (FPP) images of specular objects. Local reflections, caused by the direct reflection of the projector on smooth surfaces, often saturate pixel intensities, posing a significant obstacle to 3D shape reconstruction. The proposed method combines sinusoidal fringe projection principles with improved fitting techniques. By analyzing fringe patterns in non-highlight regions, the constant and amplitude parameters of the fringes are determined by non-highlight regions. For the critical initial phase parameter, the continuity of highlight regions and the fixed relative geometry between the projector and object are leveraged, which enables an iterative calculation strategy that progressively estimates fringe intensity within specular regions. The results show a seamless integration of the restored fringe data with the original non-highlight information, ensuring global consistency and continuity. 3D measurement experiments demonstrate effective restoration of morphological distortions and filling of point cloud holes.

A point cloud hole spiral-filling method based on 2D and 3D data fusion

Due to unfavorable factors, such as environmental noise, reflection, and occlusion of a scanned object, a point cloud obtained by structured light measurement equipment contains holes, which limits further application of the three-dimensional data. This study proposes a point cloud hole spiral-filling method based on the fusion of two- and three-dimensional data. The pixel coordinates corresponding to the point cloud are calculated according to the scanning system parameters. Based on a matrix-like distribution pattern of a camera’s pixel coordinates, the missing pixel coordinates are used to locate the hole boundary points, and the pixel coordinates of the filled points are obtained. In addition, the pixel coordinates of the hole boundary points are used as a sampling layer, which can be used to separate the outermost pixel coordinates of the filled points. Similarly, the pixel coordinates of the filling points are helically stratified. The pixel coordinates of the filling points and their neighboring points, as well as the height coordinates of the neighboring points, are used for surface interpolation to estimate the height coordinates of the filling points. The hole filling is realized after the correction of coordinates using the four-connected domain. The experimental results demonstrate that the proposed spiral hole filling algorithm can accurately identify the hole boundary and can estimate the filling points completely while preserving the details of the repair area.

Research on point cloud hole filling and 3D reconstruction in reflective area

3D reconstruction is the process of obtaining the three-dimensional shape or surface structure of an object, which is widely used in advanced manufacturing fields such as automotive, aerospace, industrial inspection, and reverse engineering. However, due to the structural characteristics of the component itself, the reflective properties of the coating material, and other factors, there may be specular reflection during image acquisition, making it difficult to achieve complete 3D reconstruction of the component. This paper proposes a method to address the problem of incomplete 3D reconstruction of strongly reflective objects by recognizing outlier points and filling point cloud holes. The proposed View-Transform-PointNet outlier point recognition network improves the alignment of the initial point cloud plane and implements secondary alignment of the point cloud based on the perpendicularity between the outlier plane in mixed reflection and the point cloud plane. The point cloud hole-filling method is based on the principle of outlier formation and approximates a local Gaussian distribution to linear variation. The distance between the end of each outlier plane and the real surface is calculated to repair the depth information of outlier points. The proposed method achieves a 39.4% increase in the number of point cloud filling, a 45.2% increase in the number of triangular mesh faces, a 46.9% increase in surface area, and a chamfer distance (CD) of 0.4471009, which is better than existing geometric repair methods in terms of standard deviation and smoothness. The method improves the alignment of initial point cloud planes and enhances the accuracy of outlier point recognition, which are the main innovative points of this study. The 3D reconstruction of the repaired point cloud model is achieved through Poisson equation and parameter adjustment. The proposed method reduces the error caused by large curvature in the boundary region and improves the smoothness and accuracy of the reconstructed model.

Observation and Process Understanding of Typical Cloud Holes Above Lakes Over the Tibetan Plateau

AbstractUnderstanding cloud distribution over lakes is crucial to determine input radiation and precipitation for lake thermal and water balance processes. Based on Himawari‐8 satellite observation and Weather Research and Forecasting model results, this study examines the influences of lakes on daytime cloud cover during warm seasons over the Tibetan Plateau (TP), which is home to thousands of lakes. Observation shows the existence of cloud holes and narrow cloud rings around the lakes, that is, fewer clouds over the lakes and more clouds along the shoreline, which has rarely been reported and is considered typical to TP by comparison with lakes outside of TP. The threshold size of lakes that can produce the shoreline cloud ring is identified to be about 300 km2, as a conservative estimate. We further highlight the importance of atmosphere advection/background wind in the formation of lake‐associated clouds. Lake breeze forms under weak background winds and the downdraft branch inhibits cloud formation over the lakes; while the updraft branch causes convection to form clouds over lakeshores. However, strong background winds do not favor lake breeze formation, changing cloud distribution over the lakes. Fewer clouds are also found in the downwind regions, and this influence is at a distance comparable to the lake scale. Due to fewer clouds over lakes, shortwave radiation is significantly larger (∼100 W/m2) and longwave radiation is smaller (∼a few W/m2) than that of the further land region. Therefore, the above observational facts provide a new perspective to advance the understanding of lake‐air interactions.

The Infrared Colors of 51 Eridani b: Micrometeoroid Dust or Chemical Disequilibrium?

We reanalyze the near-infrared spectra of the young extrasolar giant planet 51 Eridani b, which was originally presented in Macintosh et al. and Rajan et al. using modern atmospheric models, including a self-consistent treatment of disequilibrium chemistry due to turbulent vertical mixing. In addition, we investigate the possibility that significant opacity from micrometeors or other impactors in the planet’s atmosphere may be responsible for shaping the observed spectral energy distribution (SED). We find that disequilibrium chemistry is useful for describing the mid-infrared colors of the planet’s spectra, especially in regard to photometric data at the M band around 4.5 μm, which is the result of superequilibrium abundances of carbon monoxide, while the micrometeors are unlikely to play a pivotal role in shaping the SED. The best-fitting, micrometeoroid dust–free, disequilibrium chemistry, patchy cloud model has the following parameters: effective temperature T eff = 681 K with clouds (or without clouds, i.e., the grid temperature T grid = 900 K), surface gravity g = 1000 m s−2, sedimentation efficiency f sed = 10, vertical eddy diffusion coefficient K zz = 103 cm2 s−1, cloud hole fraction f hole = 0.2, and planet radius R planet = 1.0 R Jup.

Additional value-added conditional moving least squares method for point cloud hole repair

Additional value-added conditional moving least squares method for point cloud hole repair

Object defect detection based on data fusion of a 3D point cloud and 2D image

In the process of defect detection, there can be interference factors such as poor image quality and missing point cloud data as a result of the complexity of the acquisition environment. Certain limitations can be found relying solely on point cloud data processing or image feature detection. Therefore, this paper tries a more intuitive and effective exploration. Firstly, an algorithm named hole boundary points detection of point cloud, based on multi-scale principal component analysis, is proposed, which can achieve the preliminary detection of hole boundary points while calculating the normal vector of each point in the point cloud. Then the boundary contour of each hole is constructed by a polygon growth algorithm. Finally, we use the complementary information of the 3D point cloud and 2D image to explore the origin of holes and realize the ‘true’ and ‘false’ classification of holes. The experimental results show that our algorithm can successfully detect point cloud holes and can also distinguish them from object defects, providing data support for subsequent hole filling and defect measurement.

RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway

With the rapid development of the geographic information service industry, point cloud data are widely used in various fields, such as architecture, planning, cultural relics protection, mining engineering, etc. Despite that there are many approaches to collecting point clouds, we are facing the problem of point cloud holes caused by the inability of a 3D laser scanner to collect data completely in the narrow space of the mine access shaft. Thus, this paper uses RGB-D cameras to collect data and reconstruct the hole in the point cloud. We used a 3D laser scanner and RGB-D depth camera to collect the 3D point cloud data of the access shaft roadway. The maximum error was 2.617 cm and the minimum error was 0.031 cm by measuring the distance between the feature points, which satisfied the visualization repair of the missing parts of the 3D laser scanner data collection. We used the FPTH + ICP algorithm, ISS + ICP algorithm, SVD + ICP algorithm, and 3D-NDT algorithm to perform registration and fusion of the processed 3D point cloud and the original point cloud and finally repaired the hole. The study results show that the ISS + ICP registration algorithm had the most matching points and the lowest RMSE value of 13.8524 mm. In addition, in the closed and narrow roadway, the RGB-D camera was light and easy to operate and the point data acquired by it had relatively high precision. The three-dimensional point cloud of the repaired access shaft roadway has a good fit and can meet the repair requirements.

Bidirectional Point Cloud Holes Repair Obtained by SfM and Structured Light

Bidirectional Point Cloud Holes Repair Obtained by SfM and Structured Light

Automated measurement of dairy cows body size via 3D point cloud data analysis

Body measurement plays an important role in the breeding and production of dairy cows. Body measurement is mainly done manually, which is laborious, imprecise, and prone to stressful behavior in animals. A portable non-contact 3D measurement system of dairy cow body using smartphones was proposed in this study. Reconstruction of 3D point clouds scene containing the cow through a Structure-from-Motion (SfM) photogrammetry, the RANdom SAmple Consensus (RANSAC), and the Euclidean clustering were used to segment the scene and extract cow’s point clouds. Aiming to solve the holes in the cow point cloud caused by the barrier and legs occlusion, a completion method based on the spline curve with a smoothing factor was proposed. The automatic measurement of body size was realized through morphological features. The accuracy of the proposed system was evaluated in a commercial dairy farm. Compared with mesh-based method, the proposed point cloud hole completion method reduced the maximum relative errors of chest girth and chest width by 2.22% and 2.18%. Under different cow postures, the average relative error between automatic body size measurement algorithm and manual calibration was less than 4.67%. The average relative error between the measured value of the non-contact measurement system and the actual value of withers height, body length, chest girth, and chest width were 2.41%, 3.18%, 4.37%, and 6.12%, respectively. The results showed that the proposed method can be used as an automatic and non-contact approach for measuring animal body size.

Point Cloud Data Hole Repair Aggregation Algorithm Based on Optimized Neural Network

In order to solve the problem of cost cloud data and hole repair efficiency and accuracy, this article offers a study of integrated cloud network hole algorithm research based on optimal neural network. This paper first introduces a common point cloud hole-filling algorithm, provides a neural network-based point cloud blank filling algorithm, and introduces hotspot problems in a given algorithm, combined with the technology related to high-performance computing, the parallel optimization technology based on OpenMP and CUDA is adopted to accelerate the algorithm accordingly. Experiments have shown that the accuracy of pre- and postoptimization of CUDA-based algorithms varies. As the model and input point cloud data increase, the accuracy of the algorithm decreases slightly. However, when the data increases to 104 orders of magnitude, the rate of accuracy decline meets an inflection point and finally stabilizes to about 96.8% in the environment of 132457 data of monk model. The point cloud hole-filling algorithm based on the optimal neural network given in this article is highly predictable, can well repair incomplete point cloud holes, has good repair effect on point cloud holes, and can obtain high acceleration ratio, which can provide reference for practical engineering application.

Cloud-convection Feedback in Brown Dwarf Atmospheres

Numerous observational evidence has suggested the presence of active meteorology in the atmospheres of brown dwarfs. A near-infrared brightness variability has been observed. Clouds have a major role in shaping the thermal structure and spectral properties of these atmospheres. The mechanism of such variability is still unclear, and neither 1D nor global circulation models can fully study this topic due to resolution. In this study, a convective-resolving model is coupled to gray-band radiative transfer in order to study the coupling between the convective atmosphere and the variability of clouds over a large temperature range with a domain of several hundred kilometers. Six types of clouds are considered, with microphysics including settling. The clouds are radiatively active through the Rosseland mean coefficient. Radiative cloud feedback can drive spontaneous atmospheric variability in both temperature and cloud structure, as modeled for the first time in three dimensions. Silicate clouds have the most effect on the thermal structure with the generation of a secondary convective layer in some cases, depending on the assumed particle size. Iron and aluminum clouds also have a substantial impact on the atmosphere. Thermal spectra were computed, and we find the strongest effect of the clouds is the smoothing of spectral features at optical wavelengths. Compared to observed L and T dwarfs on the color–magnitude diagram, the simulated atmospheres are redder for most of the cases. Simulations with the presence of cloud holes are closer to observations.

The “Fuzzy” Repair of Urban Building Facade Point Cloud Based on Distribution Regularity

The integrity of point cloud is the basis for smoothly ensuring subsequent data processing and application. For “Smart City” and “Scan to Building Information Modeling (BIM)”, complete point cloud data is essential. At present, the most commonly used methods for repairing point cloud holes are multi-source data fusion and interpolation. However, these methods either make it difficult to obtain data, or they are ineffective at repairs or labor-intensive. To solve these problems, we proposed a point cloud “fuzzy” repair algorithm based on the distribution regularity of buildings, aiming at the façade of a building in an urban scene, especially for the vehicle Lidar point cloud. First, the point cloud was rotated to be parallel to the plane XOZ, and the feature boundaries of buildings were extracted. These boundaries were further classified as horizontal or vertical. Then, the distance between boundaries was calculated according to the Euclidean distance, and the points were divided into grids based on this distance. Finally, the holes in the grid that needed to be repaired were filled from four adjacent grids by the “copy–paste” method, and the final hole repairs were realized by point cloud smoothing. The quantitative results showed that data integrity improved after the repair and conformed to the state of the building. The angle and position deviation of the repaired grid were less than 0.54° and 3.25 cm, respectively. Compared with human–computer interaction and other methods, our method required less human intervention, and it had high efficiency. This is of promotional significance for the repair and modeling of point cloud in urban buildings.

Hole Repairing Algorithm for 3D Point Cloud Model of Symmetrical Objects Grasped by the Manipulator.

For the engineering application of manipulator grasping objects, mechanical arm occlusion and limited imaging angle produce various holes in the reconstructed 3D point clouds of objects. Acquiring a complete point cloud model of the grasped object plays a very important role in the subsequent task planning of the manipulator. This paper proposes a method with which to automatically detect and repair the holes in the 3D point cloud model of symmetrical objects grasped by the manipulator. With the established virtual camera coordinate system and boundary detection, repair and classification of holes, the closed boundaries for the nested holes were detected and classified into two kinds, which correspond to the mechanical claw holes caused by mechanical arm occlusion and the missing surface produced by limited imaging angle. These two kinds of holes were repaired based on surface reconstruction and object symmetry. Experiments on simulated and real point cloud models demonstrate that our approach outperforms the other state-of-the-art 3D point cloud hole repair algorithms.

A Novel Method for Density Analysis of Repaired Point Cloud with Holes Based on Image Data

Repairing point cloud holes has become an important problem in the research of 3D laser point cloud data, which ensures the integrity and improves the precision of point cloud data. However, for the point cloud data with non-characteristic holes, the boundary data of point cloud holes cannot be used for repairing. Therefore, this paper introduces photogrammetry technology and analyzes the density of the image point cloud data with the highest precision. The 3D laser point cloud data are first formed into hole data with sharp features. The image data are calculated into six density image point cloud data. Next, the barycenterization Bursa model is used to fine-register the two types of data and to delete the overlapping regions. Then, the cross-section is used to evaluate the precision of the combined point cloud data to get the optimal density. A three-dimensional model is constructed for this data and the original point cloud data, respectively and the surface area method and the deviation method are used to compare them. The experimental results show that the ratio of the areas is less than 0.5%, and the maximum standard deviation is 0.0036 m and the minimum is 0.0015 m.

TOWARDS MORE EFFICIENT UAS DATA ACQUISITION: CAMERA AUTO MOUNT PIVOTING OBLIQUE SURVEY

Abstract. In this paper I present a new MAVLink command, enabling oblique aerial surveys, along with its implementation on the major open source flight stacks (PX4 and ArduPilot) and ground control station (QGroundControl). A key advantage of this approach is that it enables vehicles with a typical gimbaled camera to capture oblique photos in the same pass as nadir photos, without the need for heavier and more expensive alternatives that feature multiple cameras, at fixed angles in a rigid mount, thus are unsuitable for lightweight platforms. It also allows for flexibility in the configuration of the camera angles. The principle is quite simple, the command combines camera triggering with mount actuation in a synchronized cycle along the flight traverses through the region of interest. Oblique photos have also been shown to increase the accuracy of data and help filling holes in point clouds and related outputs of surveys with vertical components. To provide evidence of its benefits, I compare the results of several missions, in simulated and field experiments, flown with nadir only surveys versus oblique surveys, and different camera configurations. In both cases, ground control and check points were used to evaluate the accuracy of the surveys. The field experiments show the vehicle had to fly 44% less with the oblique survey to cover the same area as the nadir survey, which could translate in a 80% gain in efficiency in coverage area per flight. Furthermore, this new command is capable of enhancing functionality of Unmanned Aerial Systems (UASs) without any additional hardware, therefore its adoption should be straightforward.

Small-scale structure of thermodynamic phase in Arctic mixed-phase clouds observed by airborne remote sensing during a cold air outbreak and a warm air advection event

Abstract. The combination of downward-looking airborne lidar, radar, microwave, and imaging spectrometer measurements was exploited to characterize the vertical and small-scale (down to 10 m) horizontal distribution of the thermodynamic phase of low-level Arctic mixed-layer clouds. Two cloud cases observed in a cold air outbreak and a warm air advection event observed during the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign were investigated. Both cloud cases exhibited the typical vertical mixed-phase structure with mostly liquid water droplets at cloud top and ice crystals in lower layers. The horizontal, small-scale distribution of the thermodynamic phase as observed during the cold air outbreak is dominated by the liquid water close to the cloud top and shows no indication of ice in lower cloud layers. Contrastingly, the cloud top variability in the case observed during a warm air advection showed some ice in areas of low reflectivity or cloud holes. Radiative transfer simulations considering homogeneous mixtures of liquid water droplets and ice crystals were able to reproduce the horizontal variability in this warm air advection. Large eddy simulations (LESs) were performed to reconstruct the observed cloud properties, which were used subsequently as input for radiative transfer simulations. The LESs of the cloud case observed during the cold air outbreak, with mostly liquid water at cloud top, realistically reproduced the observations. For the warm air advection case, the simulated ice water content (IWC) was systematically lower than the measured IWC. Nevertheless, the LESs revealed the presence of ice particles close to the cloud top and confirmed the observed horizontal variability in the cloud field. It is concluded that the cloud top small-scale horizontal variability is directly linked to changes in the vertical distribution of the cloud thermodynamic phase. Passive satellite-borne imaging spectrometer observations with pixel sizes larger than 100 m miss the small-scale cloud top structures.

Entrainment and Mixing in Stratocumulus: Effects of a New Explicit Subgrid-Scale Scheme for Large-Eddy Simulations with Particle-Based Microphysics

Abstract The entrainment and mixing of free-tropospheric air is an essential component of the observed microphysical structure of stratocumulus clouds. Since the relevant scales involved in this process are usually smaller than the grid spacing of typical large-eddy simulations (LESs), their correct representation is difficult. To adequately accommodate these small-scale processes, we apply a recently developed approach that explicitly simulates LES subgrid-scale (SGS) turbulence fluctuation of supersaturation using the one-dimensional linear eddy model. As a result of reduced numerical diffusion and the ability to explicitly represent the SGS distribution of liquid water and supersaturation, entrainment rates tend to be lower in the new approach compared to simulations without it. Furthermore, cloud holes comprising free-tropospheric air with negligible liquid water are shown to persist longer in the stratocumulus deck. Their mixing with the cloud is shown to be more sensitive to the microphysical composition of the cloud as a result of the explicitly resolved inhomogeneous mixing, which is also confirmed analytically. Moreover, inhomogeneous mixing is shown to decrease the droplet concentration and to increase droplet growth significantly, in contrast to previous studies. All in all, the simulations presented can be seen as a first step to bridge the gap between ultra-high-resolution direct numerical simulation and LES, allowing an appropriate representation of small-scale mixing processes, together with the large-scale dynamics of a stratocumulus system.

Rotational Light Curves of Jupiter from Ultraviolet to Mid-infrared and Implications for Brown Dwarfs and Exoplanets

Abstract Rotational modulations are observed on brown dwarfs and directly imaged exoplanets, but the underlying mechanism is not well understood. Here we analyze Jupiter’s rotational light curves at 12 wavelengths from the ultraviolet (UV) to the mid-infrared (mid-IR). The peak-to-peak amplitudes of Jupiter’s light curves range from subpercent to 4% at most wavelengths, but the amplitude exceeds 20% at 5 μm, a wavelength sensing Jupiter’s deep troposphere. Jupiter’s rotational modulations are primarily caused by discrete patterns in the cloudless belts instead of the cloudy zones. The light-curve amplitude is controlled by the sizes and brightness contrasts of the Great Red Spot (GRS), expansions of the North Equatorial Belt (NEB), patchy clouds in the North Temperate Belt (NTB), and a train of hot spots in the NEB. In reflection, the contrast is controlled by upper tropospheric and stratospheric hazes, clouds, and chromophores in the clouds. In thermal emission, the small rotational variability is caused by the spatial distribution of temperature and opacities of gas and aerosols; the large variation is caused by the NH3 cloud holes and thin-thick clouds. The methane-band light curves exhibit opposite-shape behavior compared with the UV and visible wavelengths, caused by a wavelength-dependent brightness change of the GRS. Light-curve evolution is induced by periodic events in the belts and longitudinal drifting of the GRS and patchy clouds in the NTB. This study suggests several interesting mechanisms related to distributions of temperature, gas, hazes, and clouds for understanding the observed rotational modulations on brown dwarfs and exoplanets.

Turbulence Structure in a Stratocumulus Cloud

The growth of computing power combined with advances in modeling methods can yield high-fidelity simulations establishing numerical simulation as a key tool for discovery in the atmospheric sciences. A fine-scale large-eddy simulation (LES) utilizing 1.25 m grid resolution and 5.12 × 5.12 km 2 horizontal domain is used to investigate the turbulence and liquid water structure in a stratocumulus cloud. The simulations capture the observed cloud morphology, including elongated regions of low liquid water path, cloud holes, and pockets of clear air within the cloud. The cloud can be partitioned into two broad layers with respect to the maximum mean liquid. The lower layer resembles convective turbulent structure with classical inertial range scaling of the velocity and scalar energy spectra. The top and shallower layer is directly influenced by the cloud top radiative cooling and the entrainment process. Near the cloud top, the liquid water spectra become shallower and transition to a k − 1 power law for scales smaller than about 1 km .