Quality Of Point Clouds Research Articles

PointNet-based modeling of systematic distance deviations for improved TLS accuracy

Abstract Terrestrial laser scanners (TLSs) have become indispensable for acquiring highly detailed and accurate 3D representations of the physical world. However, the acquired data is subject to systematic deviations in distance measurements due to external influences, such as distance and incidence angle. This research introduces a calibration approach by applying a deep learning model based on PointNet to predict and correct these systematic distance deviations, incorporating not only the XYZ coordinates but also additional features like intensity, incidence angle, and distances within a local neighbourhood radius of 5 cm. By predicting and subsequently correcting systematic distance deviations, the quality of TLS point clouds can be improved. Hence, our model is designed to complement and build upon the foundation of prior internal TLS calibration. A data set collected under controlled environmental conditions, containing various objects of different materials, served as the basis for training and validation the PointNet based model. In addition our analysis showcase the model’s capability to accurately model systematic distance deviations, outperforming existing methods like gradient boosting trees by capturing the spatial relationships and dependencies within the data more effectively. By defining test data sets, excluded from the training process, we underscore the ongoing effectiveness of our model’s distance measurement calibration, showcasing its ability to improve the accuracy of the TLS point cloud.

Blind Quality Assessment of Dense 3D Point Clouds with Structure Guided Resampling

Objective quality assessment of three-dimensional (3D) point clouds is essential for the development of immersive multimedia systems in real-world applications. Despite the success of perceptual quality evaluation for 2D images and videos, blind/no-reference metrics are still scarce for 3D point clouds with large-scale irregularly distributed 3D points. Therefore, in this article, we propose an objective point cloud quality index with Structure Guided Resampling (SGR) to automatically evaluate the perceptually visual quality of dense 3D point clouds. The proposed SGR is a general-purpose blind quality assessment method without the assistance of any reference information. Specifically, considering that the human visual system is highly sensitive to structure information, we first exploit the unique normal vectors of point clouds to execute regional pre-processing that consists of keypoint resampling and local region construction. Then, we extract three groups of quality-related features, including (1) geometry density features, (2) color naturalness features, and (3) angular consistency features. Both the cognitive peculiarities of the human brain and naturalness regularity are involved in the designed quality-aware features that can capture the most vital aspects of distorted 3D point clouds. Extensive experiments on several publicly available subjective point cloud quality databases validate that our proposed SGR can compete with state-of-the-art full-reference, reduced-reference, and no-reference quality assessment algorithms.

Integration and evaluation of the high-precision MotionCam-3D into a 3D thermography system

Abstract. Infrared thermal imaging enables fast, accurate and non-contact measurement of temperature distributions. However, 2D representations of 3D objects often require several images to provide significant information. For such cases, 3D thermograms allow a quick temporal and spatial analysis. In this paper, the integration of an industrial high-precision 3D sensor into a 3D thermography system is presented. The performances of the existing and new systems are assessed and compared by analyzing 3D thermograms of an industry-related test object. The geometry of the obtained point cloud is evaluated by means of a non-referenced point cloud quality assessment approach. It is shown that, in the presence of the spatial resolution and the local curvature, the proposed system performs significantly better than the existing one.

Point Cloud Quality Assessment Using a One-Dimensional Model Based on the Convolutional Neural Network.

Recent advancements in 3D modeling have revolutionized various fields, including virtual reality, computer-aided diagnosis, and architectural design, emphasizing the importance of accurate quality assessment for 3D point clouds. As these models undergo operations such as simplification and compression, introducing distortions can significantly impact their visual quality. There is a growing need for reliable and efficient objective quality evaluation methods to address this challenge. In this context, this paper introduces a novel methodology to assess the quality of 3D point clouds using a deep learning-based no-reference (NR) method. First, it extracts geometric and perceptual attributes from distorted point clouds and represent them as a set of 1D vectors. Then, transfer learning is applied to obtain high-level features using a 1D convolutional neural network (1D CNN) adapted from 2D CNN models through weight conversion from ImageNet. Finally, quality scores are predicted through regression utilizing fully connected layers. The effectiveness of the proposed approach is evaluated across diverse datasets, including the Colored Point Cloud Quality Assessment Database (SJTU_PCQA), the Waterloo Point Cloud Assessment Database (WPC), and the Colored Point Cloud Quality Assessment Database featured at ICIP2020. The outcomes reveal superior performance compared to several competing methodologies, as evidenced by enhanced correlation with average opinion scores.

Affordable Sensors for Speditive and Accurate Documentation of Built Heritage: First Tests and Preliminary Results

Abstract. When facing unregulated human interventions, the tumultuous backdrop of wars, and the relentless impact of climate change, a significant effect is held on those structures that stand as custodians of our cultural identity and historical legacy. The need to document their status is an essential first step toward preservation and restoration. This contribution will analyze and compare diverse datasets acquired through three distinct methods: Terrestrial Laser Scanner, Apple’s iPhone 15 Pro with no additional tool, and iPhone 15 Pro using Pix4D viDoc RTK Rover to assess the quality and accuracy of point clouds, as well as to determine the feasibility of each technique for the expeditious documentation of endangered built heritage. The use of mass-distributed low-cost sensors provides a rapid and cost-effective means to capture detailed 3D models of built heritage while, at the same time, democratizing the process of documentation. Low-cost sensors not only facilitate documentation but also enhance the efficiency of conservation efforts. The precision of LiDAR sensors helps identify structural vulnerabilities, allowing conservationists to prioritize interventions and allocate resources wisely. Furthermore, detailed imagery will enable conservationists to monitor subtle structural changes over time, acting as an early warning system against potential threats. As an additional aid, the RTK Rover enables centimeter-level positioning accuracy while maintaining a compact and fast deployable design, and the direct measurement of Ground Control Points (GCPs) significantly streamlines the surveying process and equipment footprint.

Single-Image 3-D Reconstruction: Rethinking Point Cloud Deformation.

Single-image 3-D reconstruction has long been a challenging problem. Recent deep learning approaches have been introduced to this 3-D area, but the ability to generate point clouds still remains limited due to inefficient and expensive 3-D representations, the dependency between the output and the number of model parameters, or the lack of a suitable computing operation. In this article, we present a novel deep-learning-based method to reconstruct a point cloud of an object from a single still image. The proposed method can be decomposed into two steps: feature fusion and deformation. The first step extracts both global and point-specific shape features from a 2-D object image, and then injects them into a randomly generated point cloud. In the second step, which is deformation, we introduce a new layer termed as GraphX that considers the interrelationship between points like common graph convolutions but operates on unordered sets. The framework can be applicable to realistic image data with background as we optionally learn a mask branch to segment objects from input images. To complement the quality of point clouds, we further propose an objective function to control the point uniformity. In addition, we introduce different variants of GraphX that cover from best performance to best memory budget. Moreover, the proposed model can generate an arbitrary-sized point cloud, which is the first deep method to do so. Extensive experiments demonstrate that we outperform the existing models and set a new height for different performance metrics in single-image 3-D reconstruction.

Evaluation of the hillslope fine-scale morphology under forest cover with pit-mound topography - Integration of geomorphometry, geophysical methods, and soil features

Forested hillslopes are zones of specific surface hydrology and geomorphic activity regimes. Their distinct properties, namely terrain microrelief (<10 m in diameter and height), are often a result of past disturbances that control forest stand conditions, soil formation processes, and superficial processes. A clear bioindicator of the past forest disturbance is pit-mound topography, which, however, is challenging to study because of its complexity and relatively small sizes of individual forms (usually <5 m in diameter). The present study analyzed the spatial representation, geomorphometric, and geophysical evaluation of the rare pit-mound topography in the Karkonosze Mountains National Park, SW Poland, Central Europe. For this task, two digital terrain models (DTMs) have been considered and were based on different quality point clouds collected during airborne and terrestrial laser scanning (ALS and TLS, respectively). The first data allowed the production of the DTM in 1 × 1 m spatial resolution, while the second data offered the DTM in 0.025 × 0.025 m resolution. Various geomorphometric derivatives (Terrain Ruggedness Index, Geomorphons, Topographic Wetness Index, Valley Depth, and Negative Openness) were applied and compared based on these models. In the further part of the study, we applied electrical resistivity tomography (ERT) and electromagnetic induction (EMI), assisted by shallow soil sampling and analyses to support the interpretation of geophysical models.Our TLS-based DTM offered higher-quality models and a better representation of pit-mound topography. The high-quality TLS-based DTM elevation model can support close-to-reality hydrological and geomorphic modeling. The geophysical investigation allowed us to isolate a critical difference between treethrow pits and mounds better represented by ERT models than shallow EMI models. The differences were partly supported by soil properties, namely lower electrical resistivity in treethrow pits were related to higher moisture conditions, organic matter, organic carbon, and silt content in pits. As a general property, pit-mound topography resulting from tree uprooting adds to the complexity of forested hillslope hydrology and geomorphic activity. The surficial heterogeneity in hillslope topography was also evident in soil properties with sharp changes in short distances between treethrow mounds and pits.

Automated cleaning of tie point clouds following USGS guidelines in Agisoft Metashape professional (ver. 2.1.0)

The U.S. Geological Survey (USGS) has published a guideline to improve the quality of digital photogrammetric reconstructions created with the widely used Agisoft Metashape Professional software. The suggested workflows aim at filtering out low-quality tie points from the tie point cloud to optimize the camera model. However, the optimization procedure relies on an iteratively performed trial-and-error approach. If manually performed, the time expenditure required from the operator can be significant and the optimization process can be affected by the degree of diligence that is applied.To minimize the time expenditure and attentiveness required from the operator and to provide a framework for an improved reproducibility of camera model optimization workflows, we present here a python script serving as an extension for Agisoft Metashape Professional (tested on version 2.1.0) that automatizes the iterative point filtering procedure proposed by the USGS. As a result, the entire processing cycle can be performed largely unattended.•A graphical user interface allows to individually adjust important camera model optimization parameters.•Main tie point cloud quality measures can be directly assessed.•The reproducibility of the automated camera model optimization as tested in this study generally is above 99%.

Cracks-suppression perceptual geometry coding for dynamic point clouds

Dynamic point clouds can effectively describe 3D objects and natural scenes, providing users with an immersive visual experience, but their huge amount of data requires efficient compression tools. To this end, the video-based point cloud compression (V-PCC) standard was developed, however, it may lead to cracks in the compressed point cloud at low bitrates. To solve the problem, this paper proposes a cracks-suppression perceptual geometry coding method for dynamic point cloud. Firstly, considering that the edge regions of 2D geometry patches in V-PCC are prone to cracks, a prior knowledge based crack region detection and preprocessing scheme is designed to reduce cracks. Secondly, considering the unique perceptual geometry characteristics of point cloud, a projected geometry curvature based structural similarity (PGC-SSIM) is proposed to evaluate geometry quality of point clouds, which contains the geometric perception information of point cloud, and is more consistent with the human visual perception. Finally, based on the PGC-SSIM, an adaptive quantization parameter adjustment strategy is designed for rate-distortion optimization in geometry coding of dynamic point clouds. The experimental results show that the proposed method can effectively reduce cracks in the compressed point cloud without reducing the coding performance compared to the V-PCC anchor. Moreover, the presented PGC-SSIM can be used to improve the visual quality of the compressed point cloud.

GMS-3DQA: Projection-Based Grid Mini-patch Sampling for 3D Model Quality Assessment

Nowadays, most three-dimensional model quality assessment (3DQA) methods have been aimed at improving accuracy. However, little attention has been paid to the computational cost and inference time required for practical applications. Model-based 3DQA methods extract features directly from the 3D models, which are characterized by their high degree of complexity. As a result, many researchers are inclined towards utilizing projection-based 3DQA methods. Nevertheless, previous projection-based 3DQA methods directly extract features from multi-projections to ensure quality prediction accuracy, which calls for more resource consumption and inevitably leads to inefficiency. Thus, in this article, we address this challenge by proposing a no-reference (NR) projection-based G rid M ini-patch S ampling 3D Model Q uality A ssessment (GMS-3DQA) method. The projection images are rendered from six perpendicular viewpoints of the 3D model to cover sufficient quality information. To reduce redundancy and inference resources, we propose a multi-projection grid mini-patch sampling strategy (MP-GMS), which samples grid mini-patches from the multi-projections and forms the sampled grid mini-patches into one quality mini-patch map (QMM). The Swin-Transformer tiny backbone is then used to extract quality-aware features from the QMMs. The experimental results show that the proposed GMS-3DQA outperforms existing state-of-the-art NR-3DQA methods on the point cloud quality assessment databases for both accuracy and efficiency. The efficiency analysis reveals that the proposed GMS-3DQA requires far less computational resources and inference time than other 3DQA competitors. The code is available at https://github.com/zzc-1998/GMS-3DQA .

PCQD‐AR: Subjective quality assessment of compressed point clouds with head‐mounted augmented reality

AbstractThis letter fully studies the coloured point cloud quality assessment in augmented reality (AR) environment through subjective test. Firstly, a point cloud dataset, named point cloud quality dataset‐AR, including ten reference point clouds and their 90 distorted versions is presented, which were generated using the reference software of video‐based point cloud compression across various combinations of geometry and texture quantization parameters. Then, the impact of geometry and texture distortions on perceived quality of point clouds in AR environment was discussed in detail. Moreover, the performance of existing objective point cloud quality assessment metrics on the proposed dataset is evaluated. The subjective dataset including the values of mean opinion score were released to public.

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.

Фильтрация шумов сканируемого LiDAR участка леса на основе цветовых моделей YCbCr и L*a*b*

Point clouds are widely used in ground-based forest scanning using LiDAR and stereo cameras. Point clouds&#x0D; often suffer from noise outliers and artifacts that distort data. Hardware accuracy and quality of the initial point cloud&#x0D; during ground scanning of a forest area can be improved by using scanners with higher expansion, as well as using&#x0D; photogrammetry or additional sensors. To eliminate noise, software methods can be used: point filtering, smoothing,&#x0D; statistical methods and reconstruction algorithms. A new approach to filtering the noise of the scanned forest area is based&#x0D; on the analysis of the values of the color components in the YCbCr- and L*a*b- spaces. The properties of the YCbCrand L*a*b-color models were investigated and threshold values for classifying points as noise or object depending on&#x0D; their distance to the centroids were determined. The use of a combined (YCbCr | L*a*b) filter on the point cloud reduced&#x0D; the number of points to 38 963 (17.41% of the original number). When calibrating the camera and LiDAR based on the&#x0D; (YCbCr | L*a*b) filter, the total average value of translation errors was 0.0247 m, rotation 6,244 degrees, reprojection&#x0D; 8,385 pixels. The noise-filtering method (YCbCr | L*a*b) shows high accuracy and reliability in removing noise and&#x0D; maintaining the integrity of objects in the point cloud, which will allow the data obtained on unmanned machines to be&#x0D; used later when performing logging operations.

Fusion-competition framework of local topology and global texture for head pose estimation

RGB image and point cloud involve texture and geometric structure, which are widely used for head pose estimation. However, images lack of spatial information, and the quality of point cloud is easily affected by sensor noise. In this paper, a novel fusion-competition framework (FCF) is proposed to overcome the limitations of a single modality. The global texture information is extracted from image and the local topology information is extracted from point cloud to project heterogeneous data into a common feature subspace. The projected texture feature weighted by the channel attention mechanism is embedded into each local point cloud region with different topological features for fusion. The scoring mechanism creates competition among the regions involving local-global fused features to predict final pose with the highest score. According to the evaluation results on the public and our constructed datasets, the FCF improves the estimation accuracy and stability by an average of 13.6 % and 12.7 %, which is compared to nine state-of-the-art methods.

Limitations of estimating branch volume from terrestrial laser scanning

Quantitative structural models (QSMs) are frequently used to simplify single tree point clouds obtained by terrestrial laser scanning (TLS). QSMs use geometric primitives to derive topological and volumetric information about trees. Previous studies have shown a high agreement between TLS and QSM total volume estimates alongside field measured data for whole trees. Although already broadly applied, the uncertainties of the combination of TLS and QSM modelling are still largely unexplored. In our study, we investigated the effect of scanning distance on length and volume estimates of branches when deriving QSMs from TLS data. We scanned ten European beech (Fagus sylvatica L.) branches with an average length of 2.6 m. The branches were scanned from distances ranging from 5 to 45 m at step intervals of 5 m from three scan positions each. Twelve close-range scans were performed as a benchmark. For each distance and branch, QSMs were derived. We found that with increasing distance, the point cloud density and the cumulative length of the reconstructed branches decreased, whereas individual volumes increased. Dependent on the QSM hyperparameters, at a scanning distance of 45 m, cumulative branch length was on average underestimated by − 75%, while branch volume was overestimated by up to + 539%. We assume that the high deviations are related to point cloud quality. As the scanning distance increases, the size of the individual laser footprints and the distances between them increase, making it more difficult to fully capture small branches and to adjust suitable QSMs.

Perception-Guided Quality Metric of 3D Point Clouds Using Hybrid Strategy.

Full-reference point cloud quality assessment (FR-PCQA) aims to infer the quality of distorted point clouds with available references. Most of the existing FR-PCQA metrics ignore the fact that the human visual system (HVS) dynamically tackles visual information according to different distortion levels (i.e., distortion detection for high-quality samples and appearance perception for low-quality samples) and measure point cloud quality using unified features. To bridge the gap, in this paper, we propose a perception-guided hybrid metric (PHM) that adaptively leverages two visual strategies with respect to distortion degree to predict point cloud quality: to measure visible difference in high-quality samples, PHM takes into account the masking effect and employs texture complexity as an effective compensatory factor for absolute difference; on the other hand, PHM leverages spectral graph theory to evaluate appearance degradation in low-quality samples. Variations in geometric signals on graphs and changes in the spectral graph wavelet coefficients are utilized to characterize geometry and texture appearance degradation, respectively. Finally, the results obtained from the two components are combined in a non-linear method to produce an overall quality score of the tested point cloud. The results of the experiment on five independent databases show that PHM achieves state-of-the-art (SOTA) performance and offers significant performance improvement in multiple distortion environments. The code is publicly available at https://github.com/zhangyujie-1998/PHM.

LLM-Guided Cross-Modal Point Cloud Quality Assessment: A Graph Learning Approach

LLM-Guided Cross-Modal Point Cloud Quality Assessment: A Graph Learning Approach

No-Reference Point Cloud Quality Assessment Through Structure Sampling and Clustering Based on Graph

No-Reference Point Cloud Quality Assessment Through Structure Sampling and Clustering Based on Graph

Point Cloud Quality Assessment Using Multi-Level Features

Point Cloud Quality Assessment Using Multi-Level Features

No-reference point cloud quality assessment based on neighbor information and attention

No-reference point cloud quality assessment based on neighbor information and attention