Ray Casting Research Articles

Reconstruction of the bending line for free-form bent components extracting the centroids and exploiting NURBS curves

Free-form bending belongs to the kinematics-based forming processes and allows the manufacturing of arbitrary 3D-bent components. To obtain the desired part, the tool kinematics is adjusted by comparing the target and obtained bending line. While the target geometry consists of parametric CAD data, the obtained geometry is a surface mesh, making the bending line extraction a challenging task. In this paper the reconstruction of the bending line for free-form bent components is presented. The strategy relies on the extraction of the centroids, for which a ray casting algorithm is developed and compared to an existing Voronoi-based method. Subsequently the obtained points are used to fit a NURBS parametric model of the curve. The algorithm parameters are investigated with a sensitivity analysis, and its performance is evaluated with a defined error metric. Finally, the strategy is validated comparing its results with a Voronoi-based algorithm, and investigating different cross-sections and geometries.

Research on spatial carving method of glutenite reservoir based on opacity voxel imaging

The glutenite reservoir in an exploration area in eastern China is well-developed and holds significant exploration potential as an important oil and gas alternative layer. However, due to the influence of sedimentary characteristics, the glutenite reservoir exhibits strong lateral heterogeneity, significant vertical thickness variations, and low accuracy in reservoir space characterization, which affects the reasonable and effective deployment of development wells. Seismic data contains the three-dimensional spatial characteristics of geological bodies, but how to design a suitable transfer function to extract the nonlinear relationship between seismic data and reservoirs is crucial. At present, the transfer functions are concentrated in low-dimensional or high-dimensional fixed mathematical models, which cannot accurately describe the nonlinear relationship between seismic data and complex reservoirs, resulting in low spatial description accuracy of complex reservoirs. In this regard, this paper first utilizes a fusion method based on probability kernel to fuse seismic attributes such as wave impedance, effective bandwidth, and composite envelope difference. This provide a more intuitive reflection of the distribution characteristics of glutenite reservoirs. Moreover, a hybrid nonlinear transfer function is established to transform the fused attribute cube into an opaque attribute cube. Finally, the illumination model and ray casting method are used to perform voxel imaging of the glutenite reservoirs, brighten the detailed characteristics of reservoir space, and then form a set of methods for ' brightening reservoirs and darkening non-reservoirs ', which improves the spatial engraving accuracy of glutenite reservoirs.

RL-NBV: A deep reinforcement learning based next-best-view method for unknown object reconstruction

The Next-Best-View (NBV) algorithm is a key component in autonomous unknown object reconstruction. It iteratively determines the optimal sensor pose to capture the maximum information about the object under reconstruction. However, prevailing deep reinforcement learning (DRL) based NBV algorithms tend to transform point cloud, the raw sensor data, into different representations, thereby obscuring natural invariances of the data. In this work, we propose an innovative DRL-based method, denoted as RL-NBV, to learn NBV policy directly from the raw point cloud data. Specifically, we interpret the observation space as the current state of the reconstructed object represented by point clouds and current view selection states. Experimental results indicate that our method outperforms existing methods in terms of reconstruction performance. Moreover, our method significantly improves efficiency over ray-casting-based algorithms as time-consuming ray casting and data transformation are unnecessary.

Multi-View Metal Parts Pose Estimation Based on a Single Camera.

Pose estimation of metal parts plays a vital role in industrial grasping areas. It is challenging to obtain complete point clouds of metal parts because of their reflective properties. This study introduces an approach for recovering the 6D pose of CAD-known metal parts from images captured by a single RGB camera. The proposed strategy only requires RGB images without depth information. The core idea of the proposed method is to use multiple views to estimate the metal parts' pose. First, the pose of metal parts is estimated in the first view. Second, ray casting is employed to simulate additional views with the corresponding status of the metal parts, enabling the calculation of the camera's next best viewpoint. The camera, mounted on a robotic arm, is then moved to this calculated position. Third, this study integrates the known camera transformations with the poses estimated from different viewpoints to refine the final scene. The results of this work demonstrate that the proposed method effectively estimates the pose of shiny metal parts.

Automated geometric quality inspection for modular boxes using BIM and LiDAR

Modular integrated construction (MiC) offers high construction quality and efficiency. Currently, geometric quality inspections for modular boxes are conducted manually. Therefore, based on BIM and LiDAR, this study proposes an automated inspection approach for outer wall surfaces and rebars of modular boxes. To extract the targeted modular boxes from complex construction sites, a novel method using scanning stations and ray casting algorithm is introduced. The point clouds of different modular boxes are then recognized and segmented into different component categories based on the pairwise registration with as-designed data. Ultimately, a reference plane-based method is proposed to automatically measure the flatness of outer wall surfaces (FOW) and protruding length of rebars (PLR). Experiments on four modular boxes show an accuracy with errors of 2.6 mm for FOW and 2.8 mm for PLR, showing the potential for enhanced quality control and efficiency of modular construction industry.

Fast iterative shooter localization in urban terrain using ray casting.

This study presents a method for real-time capable localization of impulsive acoustic events in urban terrain, omitting the need for precomputation phases, such as in time matching or impulse response matching. Using ray casting, solutions of the wave equation are computed using a boundary discretization of intersected objects, forming a weighted graph using line of sight checks, and deploying an A* search graph traversal algorithm for the calculation of the propagation time. Subsequently, the wave amplitude along the identified eigenrays is computed. The study discusses the properties and suitability of cost functions based on time differences of arrival, amplitude differences of arrival, and a combined cost function for iterative ray-based localization. The method is validated using pressure data of the muzzle blasts of various firearms collected via unattended ground sensors during an experimental campaign at the military urban combat facility in Walenstadt, Switzerland.

Assessing Learning in an Immersive Virtual Reality: A Curriculum-Based Experiment in Chemistry Education

Despite the recent advances in Virtual Reality technology and its use in education, the review of the literature shows several gaps in research on how immersive virtual environments impact the learning process. In particular, the lack of curriculum-specific experiments along with investigations of the effects of different content, activity, and interaction types in the current VR studies has been identified as a significant shortcoming. This has been more significant in STEM fields, where VR has the potential to offer engaging experiential learning opportunities. The study reported here was designed to address this gap by assessing the effect of authentic visualization and interaction types on learning a particular scientific concept. A use case scenario of “orbital hybridization” in chemistry education was selected to create this experiment and to collect data for analysis. We collected data on learning outcomes, task-completion efficiency, accuracy, and subjective usability. A combination of learning content and tasks designed based on the relevant educational theories was presented to three groups: 2D, VR interaction type 1 (hand gestures), and VR interaction type 2 (ray casting). The results showed that VR could improve learning and that interaction type could influence efficiency and accuracy depending on the task.

Visibility-based layout of a hospital unit - An optimization approach.

Apatient fall is one of the adverse events in an inpatient unit of a hospital that can lead to disability and/or mortality. Themedical literature suggests that increased visibility of patients by unit nurses is essential to improve patient monitoring and, in turn, reduce falls. However, such research has been descriptive in nature and does not provide an understanding of the characteristics of an optimal inpatient unit layout from a visibility-standpoint. To fill this gap, we adopt an interdisciplinary approach that combines the human field of view with facility layout design approaches. Specifically, we propose a bi-objective optimization model that jointly determines the optimal (i) location of a nurse in a nursing station and (ii) orientation of a patient's bed in a room for a given layout. The two objectives are maximizing the total visibility of all patients across patient rooms and minimizing inequity in visibility among those patients. We consider three different layout types, L-shaped, I-shaped, and Radial; these shapes exhibit the section of an inpatient unit that a nurse oversees. To estimate visibility, we employ the ray casting algorithm to quantify the visible target in a room when viewed by the nurse from the nursing station. The algorithm considers nurses' horizontal visual field and their depth of vision. Owing to the difficulty in solving the bi-objective model, we also propose a Multi-Objective Particle Swarm Optimization (MOPSO) heuristic to find (near) optimal solutions. Our findings suggest that the Radial layout appears to outperform the other two layouts in terms of the visibility-based objectives. We found that with a Radial layout, there can be an improvement of up to 50% in equity measure compared to an I-shaped layout. Similar improvements were observed when compared to the L-shaped layout as well. Further, the position of the patient's bed plays a role in maximizing the visibility of the patient's room.Insights from our work will enable understanding and quantifying the relationship between a physical layout and the corresponding provider-to-patient visibility to reduce adverse events.

A Fast and Interactive Augmented Reality System for PET/CT-guided Intervention of Neuroblastoma.

Neuroblastoma is the most common type of extracranial solid tumor in children and can often result in death if not treated. High-intensity focused ultrasound (HIFU) is a non-invasive technique for treating tissue that is deep within the body. It avoids the use of ionizing radiation, avoiding long-term side-effects of these treatments. The goal of this project was to develop the rendering component of an augmented reality (AR) system with potential applications for image-guided HIFU treatment of neuroblastoma. Our project focuses on taking 3D models of neuroblastoma lesions obtained from PET/CT and displaying them in our AR system in near real-time for use by physicians. We used volume ray casting with raster graphics as our preferred rendering method, as it allows for the real-time editing of our 3D radiologic data. Some unique features of our AR system include intuitive hand gestures and virtual user interfaces that allow the user to interact with the rendered data and process PET/CT images for optimal visualization. We implemented the feature to set a custom transfer function, set custom intensity cutoff points, and region-of-interest extraction via cutting planes. In the future, we hope to incorporate this work as part of a complete system for focused ultrasound treatment by adding ultrasound simulation, visualization, and deformable registration.

Playing Flappy Bird Based on Motion Recognition Using a Transformer Model and LIDAR Sensor.

A transformer neural network is employed in the present study to predict Q-values in a simulated environment using reinforcement learning techniques. The goal is to teach an agent to navigate and excel in the Flappy Bird game, which became a popular model for control in machine learning approaches. Unlike most top existing approaches that use the game's rendered image as input, our main contribution lies in using sensory input from LIDAR, which is represented by the ray casting method. Specifically, we focus on understanding the temporal context of measurements from a ray casting perspective and optimizing potentially risky behavior by considering the degree of the approach to objects identified as obstacles. The agent learned to use the measurements from ray casting to avoid collisions with obstacles. Our model substantially outperforms related approaches. Going forward, we aim to apply this approach in real-world scenarios.

Examination of ray casting in Unity 3D as a fast pose prediction tool for industrial CT scans of multi-material specimens

Examination of ray casting in Unity 3D as a fast pose prediction tool for industrial CT scans of multi-material specimens

Web-based simulation and motion planning for human-robot and multi-robot applications

ABSTRACT This paper introduces the human-robot and multi-robot programming and simulation capabilities of a web-based, open-source robot programming and simulation tool called Assembly. The tool supports scenarios with up to three robots and provides support for simulating human arms working closely with a robot. To facilitate the simulation of scenarios involving human-robot and robot-robot collaboration, the paper proposes an improved version of the neural network-based Adaptive Simplex Motion Planner, which is capable of performing motion planning at a rate of over 3500 Hz. The improved toolpath planner uses an obstacle avoidance approach that leverages ray casting and shadow volume teachniques. The new planner is evaluated on the basis of two human-robot and multi-robot simulation scenarios and the results are compared to those of the original algorithm.

Improving Efficiency of Iso-Surface Extraction on Implicit Neural Representations Using Uncertainty Propagation.

Implicit Neural representations (INRs) are widely used for scientific data reduction and visualization by modeling the function that maps a spatial location to a data value. Without any prior knowledge about the spatial distribution of values, we are forced to sample densely from INRs to perform visualization tasks like iso-surface extraction which can be very computationally expensive. Recently, range analysis has shown promising results in improving the efficiency of geometric queries, such as ray casting and hierarchical mesh extraction, on INRs for 3D geometries by using arithmetic rules to bound the output range of the network within a spatial region. However, the analysis bounds are often too conservative for complex scientific data. In this paper, we present an improved technique for range analysis by revisiting the arithmetic rules and analyzing the probability distribution of the network output within a spatial region. We model this distribution efficiently as a Gaussian distribution by applying the central limit theorem. Excluding low probability values, we are able to tighten the output bounds, resulting in a more accurate estimation of the value range, and hence more accurate identification of iso-surface cells and more efficient iso-surface extraction on INRs. Our approach demonstrates superior performance in terms of the iso-surface extraction time on four datasets compared to the original range analysis method and can also be generalized to other geometric query tasks.

PROCEEDINGS IN UAS-ASSISTED BRIDGE INSPECTIONS: RTK-BASED PHOTOGRAMMETRIC RECONSTRUCTION AND SPATIAL FILTERING

Abstract. Traditional bridge inspections present considerable challenges in terms of efficiency and accuracy. However, recent advancements in Unmanned Aerial Systems (UASs) and deep learning for object detection have opened up new avenues for automatic bridge damage detection. We present a comprehensive framework leveraging these technologies for automated damage detection in UAS imagery, followed by accurate mapping of the damage predictions on photogrammetric models. In this work, we propose a photogrammetric procedure to retrieve geolocated bridge models solely based on Real-Time Kinematics (RTK) information. Within the damage detection step, we conduct extensive testing and optimization of model hyperparameters using YOLOv8 and Slicing Aided Hyper Interference (SAHI). Next, we map the predictions onto the 3D model using ray casting, allowing to group and filter the predictions by their area and position. Finally, a Graphical User Interface (GUI) allows bridge inspectors to identify false positive predictions, generate new training data, and directly measure damage dimensions in the images. Validation on a concrete box girder bridge resulted in a photogrammetric model with a mean error of 1.3 cm, negating the need for ground control points. Our model training process revealed substantial performance variations between training and test datasets, underscoring the importance of evaluating optimal hyperparameters on UAS inspection images rather than relying on the validation metrics. Lastly, we successfully map the detected damage and create new training data from the UAS inspection images. This framework significantly enhances bridge inspection accuracy and efficiency, providing a strong foundation for future developments in automated bridge inspections.

Efficient Construction of Voxel Models for Ore Bodies Using an Improved Winding Number Algorithm and CUDA Parallel Computing

The three-dimensional (3D) geological voxel model is essential for numerical simulation and resource calculation. However, it can be challenging due to the point in polygon test in 3D voxel modeling. The commonly used Winding number algorithm requires the manual setting of observation points and uses their relative positions to restrict the positive and negative solid angles. Therefore, we proposed the Winding number with triangle network coding (WNTC) algorithm and applied it to automatically construct a 3D voxel model of the ore body. The proposed WNTC algorithm encodes the stratum model by using the Delaunay triangulation network to constrain the index order of each vertex of the triangular plane unit. GPU parallel computing was used to optimize its computational speed. Our results demonstrated that the WNTC algorithm can greatly improve the efficiency and automation of 3D ore body modeling. Compared to the Ray casting method, it can compensate for a voxel loss of about 0.7%. We found the GPU to be 99.96% faster than the CPU, significantly improving voxel model construction speed. Additionally, this method is less affected by the complexity of the stratum model. Our study has substantial potential for similar work in 3D geological modeling and other relevant fields.

3D-2D Distance Maps Conversion Enhances Classification of Craniosynostosis.

Diagnosis of craniosynostosis using photogrammetric 3D surface scans is a promising radiation-free alternative to traditional computed tomography. We propose a 3D surface scan to 2D distance map conversion enabling the usage of the first convolutional neural networks (CNNs)-based classification of craniosynostosis. Benefits of using 2D images include preserving patient anonymity, enabling data augmentation during training, and a strong under-sampling of the 3D surface with good classification performance. The proposed distance maps sample 2D images from 3D surface scans using a coordinate transformation, ray casting, and distance extraction. We introduce a CNNbased classification pipeline and compare our classifier to alternative approaches on a dataset of 496 patients. We investigate into low-resolution sampling, data augmentation, and attribution mapping. Resnet18 outperformed alternative classifiers on our dataset with an F1-score of 0.964 and an accuracy of 98.4 %. Data augmentation on 2D distance maps increased performance for all classifiers. Under-sampling allowed 256-fold computation reduction during ray casting while retaining an F1-score of 0.92. Attribution maps showed high amplitudes on the frontal head. We demonstrated a versatile mapping approach to extract a 2D distance map from the 3D head geometry increasing classification performance, enabling data augmentation during training on 2D distance maps, and the usage of CNNs. We found that low-resolution images were sufficient for a good classification performance. Photogrammetric surface scans are a suitable craniosynostosis diagnosis tool for clinical practice. Domain transfer to computed tomography seems likely and can further contribute to reducing ionizing radiation exposure for infants.

A Modified Beer–Lambert–Bouguer Law for Nonrandom Distributions and Its Application in Gap Probability Calculations for Heterogeneous Canopies

AbstractThe three‐dimensional (3D) structure of vegetation canopy strongly influence the absorption and reflection of radiation at the land surface. The gap probability is one primary indicator of the canopy structure. For homogeneous canopies, where the plant elements are distributed completely randomly and statistically independent, the gap probability can be modeled with the Beer–Lambert–Bouguer law. However, this method is not suitable for nonrandom distributions. A physically based and computationally efficient algorithm is developed to address this limitation, by introducing the pair correlation function to describe the nonrandom distribution pattern. Pair correlation function is based on the distribution of distances of pairs of points, and can therefore describe random, clumped, or regular distribution patterns, and detect mixed patterns at different scales. In this paper, we take tree crown as the basic structure element, and apply the new method to calculate the gap probability. The simulations agree well with results obtained by the ray casting method for various distribution patterns. The results show that horizontal distribution of trees is one of the key factors impacts the gap probability. For the case when different patterns coexist on various scales, we can ignore the distribution pattern at larger scale, and focus on the pattern at smaller scale. When zenith angel is large, the azimuthal dependency of the gap probability should be noted.

Physical vapor deposition simulator by graphical processor unit ray casting

This paper presents fast, accurate software for modeling physical vapor deposition systems over irregular surfaces. The model is implemented using graphics processing unit (GPU) ray casting. Applied models are viewed as a cross section of the area of interest. Given evaporation rate, time, and angular profiles in a vacuum system, an iterative time-step approach for calculating deposition profiles is calculated in the GPU architecture following a ballistic modeling approach. Thin-film technologies for the electronics industry will require evaporations on complex surfaces. Depending on the nature of the surface, a uniform thin film across the topology is wanted for various device parameters. The ray casting method is tested against various profiles. The code is freely distributed on GitHub (see https://github.com/adam-r-thomas/PVDS).

A fast analytical dose calculation approach for MRI-guided proton therapy.

Objective.Magnetic resonance (MR) is an innovative technology for online image guidance in conventional radiotherapy and is also starting to be considered for proton therapy as well. For MR-guided therapy, particularly for online plan adaptations, fast dose calculation is essential. Monte Carlo (MC) simulations, however, which are considered the gold standard for proton dose calculations, are very time-consuming. To address the need for an efficient dose calculation approach for MRI-guided proton therapy, we have developed a fast GPU-based modification of an analytical dose calculation algorithm incorporating beam deflections caused by magnetic fields.Approach.Proton beams (70-229 MeV) in orthogonal magnetic fields (0.5/1.5 T) were simulated using TOPAS-MC and central beam trajectories were extracted to generate look-up tables (LUTs) of incremental rotation angles as a function of water-equivalent depth. Beam trajectories are then reconstructed using these LUTs for the modified ray casting dose calculation. The algorithm was validated against MC in water, different materials and for four example patient cases, whereby it has also been fully incorporated into a treatment plan optimisation regime.Main results.Excellent agreement between analytical and MC dose distributions could be observed with sub-millimetre range deviations and differences in lateral shifts <2 mm even for high densities (1000 HU). 2%/2 mm gamma pass rates were comparable to the 0 T scenario and above 94.5% apart for the lung case. Further, comparable treatment plan quality could be achieved regardless of magnetic field strength.Significance.A new method for accurate and fast proton dose calculation in magnetic fields has been developed and successfully implemented for treatment plan optimisation.

Semiautomated Segmentation and Analysis of Airway Lumen in Pediatric Patients Using Ultra Short Echo Time MRI

Ultra short echo time (UTE) magnetic resonance imaging (MRI) pulse sequences have shown promise for airway assessment, but the feasibility and repeatability in the pediatric lung are unknown. The purpose of this work was to develop a semiautomated UTE MRI airway segmentation pipeline from the trachea-to-tertiary airways in pediatric participants and assess repeatability and lumen diameter correlations to lung function. A total of 29 participants (n=7 healthy, n=11 cystic fibrosis, n=6 asthma, and n=5 ex-preterm), aged 7-18 years, were imaged using a 3D stack-of-spirals UTE examination at 3 T. Two independent observers performed airway segmentations using a pipeline developed in-house; observer 1 repeated segmentations 1 month later. Segmentations were extracted using region-growing with leak detection, then manually edited if required. The airway trees were skeletonized, pruned, and labeled. Airway lumen diameter measurements were extracted using ray casting. Intra- and interobserver variability was assessed using the Sørensen-Dice coefficient (DSC) and intra-class correlation coefficient (ICC). Correlations between lumen diameter and pulmonary function were assessed using Spearman's correlation coefficient. For airway segmentations and lumen diameter, intra- and interobserver DSCs were 0.88 and 0.80, while ICCs were 0.95 and 0.89, respectively. The variability increased from the trachea-to-tertiary airways for intra- (DSC: 0.91-0.64; ICC: 0.91-0.49) and interobserver (DSC: 0.84-0.51; ICC: 0.89-0.21) measurements. Lumen diameter was significantly correlated with forced expiratory volume in 1secondand forced vital capacity (P<.05). UTE MRI airway segmentation from the trachea-to-tertiary airways in pediatric participants across a range of diseases is feasible. The UTE MRI-derived lumen measurements were repeatable and correlated with lung function.