Accurate Surface Reconstruction Research Articles

Accurate and efficient surface reconstruction from volume fraction data on general meshes

Simulations involving free surfaces and fluid interfaces are important in many areas of engineering. There is, however, still a need for improved simulation methods. Recently, a new efficient geometric VOF method called isoAdvector for general polyhedral meshes was published. We investigate the interface reconstruction step of isoAdvector, and demonstrate that especially for unstructured meshes the applied isosurface based approach can lead to noisy interface orientations. We then introduce a novel computational interface reconstruction scheme based on calculation of a reconstructed distance function (RDF). By iterating over the RDF calculation and interface reconstruction, we obtain second order convergence of both the interface normal and position within cells even with a strict L∞ error norm. In 2D this is verified with reconstruction of a circle on Cartesian meshes and on unstructured triangular and polygonal prism meshes. In 3D the second order convergence is verified with reconstruction of a sphere on Cartesian meshes and on unstructured tetrahedral and polyhedral meshes. The new scheme is combined with the interface advection step of the isoAdvector algorithm. Significantly reduced absolute advection errors are obtained, and for CFL number 0.2 and below we demonstrate second order convergence on all the mentioned mesh types in 2D and 3D. The implementation of the proposed interface reconstruction schemes is straightforward and the computational cost is significantly reduced compared to contemporary methods. The schemes are implemented as an extension to the Computational Fluid Dynamics (CFD) Open Source software package, OpenFOAM®. The extension module and all test cases presented in this paper are released as open source.

Gradient-based 3D skin roughness rendering from an in-vivo skin image for dynamic haptic palpation.

Many studies have revealed the importance of palpation for dermatologists; however, palpation is not always possible due to the risk of secondary infections or the risk of damaging the affected area. Thus, haptic rendering for indirect palpation using in-vivo skin images, which will enable to examine a real three-dimensional (3D) skin sample by virtual touch without directly palpating the infected skin area, could be a useful technology in dermatology. We propose a new method of accurate 3D skin surface reconstruction using simple gradients from a single skin image for accurate 3D roughness rendering with a haptic device. Our approach takes advantage of bilateral filtering to preserve skin roughness and image gradients in order to generate a 3D skin surface (polygonal meshes) while preserving skin wrinkles and rough surface textures. Our method was evaluated using two experiments. The accuracy was tested with six 3D ground-truth surfaces and four clinical skin images (acne, miliaria, sweet syndrome, and herpes simplex). For objective evaluation, a well-known 3D roughness estimation method and the Hausdorff distance were adopted to compute errors. All results showed that the accuracy of our method is superior to that of the two existing methods. Haptic roughness rendering for skin palpation examination requires efficient and accurate 3D surface reconstruction. In this study, we developed a new method that can be used to reconstruct a 3D skin surface accurately while preserving roughness through the use of a single skin image.

A Global Optimization Method for Specular Highlight Removal From a Single Image

The presence of specular highlight is a critical issue for both natural and medical images such as those produced by laparoscopes, which can lead to erroneous visual tracking, stereo reconstruction, and image segmentation. Specular highlight removal from a single image is necessary for image analysis and applications. Due to the differences between natural and medical image scenes, existing literature to address this issue has only been effective on natural images or medical images with textureless regions. To overcome this limitation, we propose a global optimization method for specular highlight removal from a single image based on a dichromatic reflection model. In addition to introducing modified illumination chromaticity, the proposed method consists of two novel steps: one for estimating diffuse chromaticity by correcting hue and saturation on highlighted regions, and the other for estimating diffuse and specular reflection coefficients using convex optimization with double regularization. The estimated diffuse chromaticity is proven to approximate the true diffuse chromaticity and the proposed optimization algorithm is guaranteed to find the optimal diffuse coefficients. Experimental results show that the proposed method can effectively remove specular highlights from both natural images and endoscopic images with texture detail preservation. To further demonstrate the efficacy of our proposed method, an application of stereo reconstruction using a public dataset illustrates that our highlight removal method can enhance surface reconstruction accuracy from 1.10mm RMSD to 0.69mm RMSD.

Advanced 3D Photogrammetric Surface Reconstruction of Extensive Objects by UAV Camera Image Acquisition.

This paper proposes a replicable methodology to enhance the accuracy of the photogrammetric reconstruction of large-scale objects based on the optimization of the procedures for Unmanned Aerial Vehicle (UAV) camera image acquisition. The relationships between the acquisition grid shapes, the acquisition grid geometric parameters (pitches, image rates, camera framing, flight heights), and the 3D photogrammetric surface reconstruction accuracy were studied. Ground Sampling Distance (GSD), the necessary number of photos to assure the desired overlapping, and the surface reconstruction accuracy were related to grid shapes, image rate, and camera framing at different flight heights. The established relationships allow to choose the best combination of grid shapes and acquisition grid geometric parameters to obtain the desired accuracy for the required GSD. This outcome was assessed by means of a case study related to the ancient arched brick Bridge of the Saracens in Adrano (Sicily, Italy). The reconstruction of the three-dimensional surfaces of this structure, obtained by the efficient Structure-From-Motion (SfM) algorithms of the commercial software Pix4Mapper, supported the study by validating it with experimental data. A comparison between the surface reconstruction with different acquisition grids at different flight heights and the measurements obtained with a 3D terrestrial laser and total station-theodolites allowed to evaluate the accuracy in terms of Euclidean distances.

Space-filling scan paths and Gaussian process-aided adaptive sampling for efficient surface measurements

A method combining space-filling scan paths and adaptive sampling is proposed for surface measurements. Scan paths including a fractal Hilbert curve and a spiral pattern are mainly investigated. The adaptive sampling is based on iterative Gaussian process (GP) inference. Sampling positions are intelligently suggested along the scan path and the final sampled data are trained in a GP-model to reconstruct the entire topography. Simulations and experiments on different surfaces demonstrated the capability of the proposed method. When the special scan paths are employed alone, the required data amount is reduced to about 10%–13% of the uniform sampling and the relative error of surface reconstruction is within 10%. If the GP-aided adaptive sampling is further integrated, the data amount can be reduced to approximately 3%–4%. In addition, time-consumption in scanning is significantly eliminated. Compared with the raster scan, the integration of special scan paths and GP-aided adaptive sampling has several prominent advantages such as eliminating data amount, preserving surface reconstruction accuracy, maintaining a single-pass scan and saving time-cost. The measurement method has a potential application in situations where the efficiency is of critical importance.

Model-free deflectometry for freeform optics measurement using an iterative reconstruction technique.

We present a novel model-free iterative data-processing approach that improves surface reconstruction accuracy for deflectometry tests of unknown surfaces. This new processing method iteratively reconstructs the surface, leading to reduced error in the final reconstructed surface. The method was implemented in a deflectometry system, and a freeform surface was tested and compared to interferometric test results. The reconstructed departure from interferometric results was reduced from 15.80μm RMS with model-based deflectometry down to 5.20μm RMS with the iterative technique reported here.

Surface reconstruction accuracy using ultrasonic arrays: Application to non-destructive testing

The accurate non-destructive inspection of engineering structures using ultrasonic immersion imaging requires a precise representation of the surface of the structure. Here we investigate the relationship between surface geometry, surface measurement error using ultrasonic arrays and the total focusing method (TFM) and how this impacts on the ability to image a feature within a component. Surfaces shaped as sinusoids covering combinations of surface wavelengths (0.8 to 32λwater) and amplitudes (0.6 to 9λwater) are studied. The surface reconstruction errors are shown to cause errors in imaging, such as reduced amplitude and blurring of the image of a side-drilled hole. These reconstruction errors are shown to increase rapidly with the maximum gradient of the sinusoid. Sinusoidal surfaces with maximum gradients <45° lead to average surface reconstruction errors <λwater and amplitude imaging errors within 6 dB of the flat-surface case. It is also shown that very poor results are obtained if the surface gradient is excessively steep.

The impact of B1+ correction on MP2RAGE cortical T1 and apparent cortical thickness at 7T.

Determination of cortical thickness using MRI has often been criticized due to the presence of various error sources. Specifically, anatomical MRI relying on T1 contrast may be unreliable due to spatially variable image contrast between gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). Especially at ultra‐high field (≥ 7T) MRI, transmit and receive B1‐related image inhomogeneities can hamper correct classification of tissue types. In the current paper, we demonstrate that residual B1+ (transmit) inhomogeneities in the T1‐weighted and quantitative T1 images using the MP2RAGE sequence at 7T lead to biases in cortical thickness measurements. As expected, post‐hoc correction for the spatially varying B1+ profile reduced the apparent T1 values across the cortex in regions with low B1+, and slightly increased apparent T1 in regions with high B1+. As a result, improved contrast‐to‐noise ratio both at the GM‐CSF and GM‐WM boundaries can be observed leading to more accurate surface reconstructions and cortical thickness estimates. Overall, the changes in cortical thickness ranged between a 5% decrease to a 70% increase after B1+ correction, reducing the variance of cortical thickness values across the brain dramatically and increasing the comparability with normative data. More specifically, the cortical thickness estimates increased in regions characterized by a strong decrease of apparent T1 after B1+ correction in regions with low B1+ due to improved detection of the pial surface. The current results suggest that cortical thickness can be more accurately determined using MP2RAGE data at 7T if B1+ inhomogeneities are accounted for.

Intelligent adaptive sampling guided by Gaussian process inference

With the aim of reducing sampling density while having minimal impact on surface reconstruction accuracy, an adaptive sampling method based on Gaussian process inference is proposed. In each iterative step, the current sampling points serve as the training data to predict surface topography and then a new sampling point is adaptively located and inserted at the position where the maximum inference uncertainty is estimated. The updated samples are trained in the next step. By such an iterative training–inference–sampling approach, the reconstructed topography can converge to the expected one efficiently. Demonstrations on different structured, freeform and roughness surfaces ascertain the effectiveness of the sampling strategy. It can lead to an accurate inference of the surface topography and a sufficient reduction of data points compared with conventional uniform sampling. Robustness against random surface features, measurement noise and sharp height changes is further discussed. Such an adaptive sampling method is extremely suitable for discrete point-by-point measurements.

Improved zonal integration method for high accurate surface reconstruction in quantitative deflectometry.

In quantitative deflectometry, a test optical component is generally divided into numerous sample regions by the pixels on a camera's CCD detector, and the adjacent intervals of sample regions are unequal in off-axis configurations. In this case, errors will be introduced in the reconstruction result if the traditional Southwell zonal integration method is arbitrarily used. Thus, an improved zonal method is proposed in this paper. Both simulations and experiments are conducted to demonstrate the validity and accuracy of the improved zonal method. In the simulations, compared with the traditional zonal method, the reconstruction accuracy for three different figures of sphere, hyperbolic, and flat surfaces using our proposed method is obviously improved, especially when the aperture of the test optics is not rectangular, but circular. Experimental results also show that when we integrate the slopes measured at unequal-spaced sampling points, the proposed zonal method is superior to the traditional zonal method in accuracy; meanwhile, it has advantages over the modal methods in reconstructing local detail information, such as a slight surface scratch, on the test optical component.

A New Approach for Realistic 3D Reconstruction of Planar Surfaces from Laser Scanning Data and Imagery Collected Onboard Modern Low-Cost Aerial Mapping Systems

Over the past few years, accurate 3D surface reconstruction using remotely-sensed data has been recognized as a prerequisite for different mapping, modelling, and monitoring applications. To fulfill the needs of these applications, necessary data are generally collected using various digital imaging systems. Among them, laser scanners have been acknowledged as a fast, accurate, and flexible technology for the acquisition of high density 3D spatial data. Despite their quick accessibility, the acquired 3D data using these systems does not provide semantic information about the nature of scanned surfaces. Hence, reliable processing techniques are employed to extract the required information for 3D surface reconstruction. Moreover, the extracted information from laser scanning data cannot be effectively utilized due to the lack of descriptive details. In order to provide a more realistic and accurate perception of the scanned scenes using laser scanning systems, a new approach for 3D reconstruction of planar surfaces is introduced in this paper. This approach aims to improve the interpretability of the extracted planar surfaces from laser scanning data using spectral information from overlapping imagery collected onboard modern low-cost aerial mapping systems, which are widely adopted nowadays. In this approach, the scanned planar surfaces using laser scanning systems are initially extracted through a novel segmentation procedure, and then textured using the acquired overlapping imagery. The implemented texturing technique, which intends to overcome the computational inefficiency of the previously-developed 3D reconstruction techniques, is performed in three steps. In the first step, the visibility of the extracted planar surfaces from laser scanning data within the collected images is investigated and a list of appropriate images for texturing each surface is established. Successively, an occlusion detection procedure is carried out to identify the occluded parts of these surfaces in the field of view of captured images. In the second step, visible/non-occluded parts of the planar surfaces are decomposed into segments that will be textured using individual images. Finally, a rendering procedure is accomplished to texture these parts using available images. Experimental results from overlapping laser scanning data and imagery collected onboard aerial mapping systems verify the feasibility of the proposed approach for efficient realistic 3D surface reconstruction.

Comprehensive Use of Curvature for Robust and Accurate Online Surface Reconstruction.

Interactive real-time scene acquisition from hand-held depth cameras has recently developed much momentum, enabling applications in ad-hoc object acquisition, augmented reality and other fields. A key challenge to online reconstruction remains error accumulation in the reconstructed camera trajectory, due to drift-inducing instabilities in the range scan alignments of the underlying iterative-closest-point (ICP) algorithm. Various strategies have been proposed to mitigate that drift, including SIFT-based pre-alignment, color-based weighting of ICP pairs, stronger weighting of edge features, and so on. In our work, we focus on surface curvature as a feature that is detectable on range scans alone and hence does not depend on accurate multi-sensor alignment. In contrast to previous work that took curvature into consideration, however, we treat curvature as an independent quantity that we consistently incorporate into every stage of the real-time reconstruction pipeline, including densely curvature-weighted ICP, range image fusion, local surface reconstruction, and rendering. Using multiple benchmark sequences, and in direct comparison to other state-of-the-art online acquisition systems, we show that our approach significantly reduces drift, both when analyzing individual pipeline stages in isolation, as well as seen across the online reconstruction pipeline as a whole.

Classified denoising method for laser point cloud data of stored grain bulk surface based on discrete wavelet threshold

Surfaces of stored grain bulk are often reconstructed from organized point sets with noise by 3-D laser scanner in an online measuring system. As a result, denoising is an essential procedure in processing point cloud data for more accurate surface reconstruction and grain volume calculation. A classified denoising method was presented in this research for noise removal from point cloud data of the grain bulk surface. Based on the distribution characteristics of cloud point data, the noisy points were divided into three types: The first and second types of the noisy points were either sparse points or small point cloud data deviating and suspending from the main point cloud data, which could be deleted directly by a grid method; the third type of the noisy points was mixed with the main body of point cloud data, which were most difficult to distinguish. The point cloud data with those noisy points were projected into a horizontal plane. An image denoising method, discrete wavelet threshold (DWT) method, was applied to delete the third type of the noisy points. Three kinds of denoising methods including average filtering method, median filtering method and DWT method were applied respectively and compared for denoising the point cloud data. Experimental results show that the proposed method remains the most of the details and obtains the lowest average value of RMSE (Root Mean Square Error, 0.219) as well as the lowest relative error of grain volume (0.086%) compared with the other two methods. Furthermore, the proposed denoising method could not only achieve the aim of removing noisy points, but also improve self-adaptive ability according to the characteristics of point cloud data of grain bulk surface. The results from this research also indicate that the proposed method is effective for denoising noisy points and provides more accurate data for calculating grain volume. Keywords: point cloud data, denoising, grid method, discrete wavelet threshold (DWT) method, 3-D laser scanning, stored grain DOI: 10.3965/j.ijabe.20160904.2333 Citation: Shao Q, Xu T, Yoshino T, Song N, Zhu H. Classified denoising method for laser point cloud data of stored grain bulk surface based on discrete wavelet threshold. Int J Agric & Biol Eng, 2016; 9(4): 123－131.

ACCURACY OF 3D RECONSTRUCTION IN AN ILLUMINATION DOME

Abstract. The accuracy of 3D surface reconstruction was compared from image sets of a Metric Test Object taken in an illumination dome by two methods: photometric stereo and improved structure-from-motion (SfM), using point cloud data from a 3D colour laser scanner as the reference. Metrics included pointwise height differences over the digital elevation model (DEM), and 3D Euclidean differences between corresponding points. The enhancement of spatial detail was investigated by blending high frequency detail from photometric normals, after a Poisson surface reconstruction, with low frequency detail from a DEM derived from SfM.

ACCURACY OF 3D RECONSTRUCTION IN AN ILLUMINATION DOME

The accuracy of 3D surface reconstruction was compared from image sets of a Metric Test Object taken in an illumination dome by two methods: photometric stereo and improved structure-from-motion (SfM), using point cloud data from a 3D colour laser scanner as the reference. Metrics included pointwise height differences over the digital elevation model (DEM), and 3D Euclidean differences between corresponding points. The enhancement of spatial detail was investigated by blending high frequency detail from photometric normals, after a Poisson surface reconstruction, with low frequency detail from a DEM derived from SfM.

QUALITY ANALYSIS OF 3D SURFACE RECONSTRUCTION USING MULTI-PLATFORM PHOTOGRAMMETRIC SYSTEMS

Abstract. In recent years, the necessity of accurate 3D surface reconstruction has been more pronounced for a wide range of mapping, modelling, and monitoring applications. The 3D data for satisfying the needs of these applications can be collected using different digital imaging systems. Among them, photogrammetric systems have recently received considerable attention due to significant improvements in digital imaging sensors, emergence of new mapping platforms, and development of innovative data processing techniques. To date, a variety of techniques haven been proposed for 3D surface reconstruction using imagery collected by multi-platform photogrammetric systems. However, these approaches suffer from the lack of a well-established quality control procedure which evaluates the quality of reconstructed 3D surfaces independent of the utilized reconstruction technique. Hence, this paper aims to introduce a new quality assessment platform for the evaluation of the 3D surface reconstruction using photogrammetric data. This quality control procedure is performed while considering the quality of input data, processing procedures, and photo-realistic 3D surface modelling. The feasibility of the proposed quality control procedure is finally verified by quality assessment of the 3D surface reconstruction using images from different photogrammetric systems.

QUALITY ANALYSIS OF 3D SURFACE RECONSTRUCTION USING MULTI-PLATFORM PHOTOGRAMMETRIC SYSTEMS

In recent years, the necessity of accurate 3D surface reconstruction has been more pronounced for a wide range of mapping, modelling, and monitoring applications. The 3D data for satisfying the needs of these applications can be collected using different digital imaging systems. Among them, photogrammetric systems have recently received considerable attention due to significant improvements in digital imaging sensors, emergence of new mapping platforms, and development of innovative data processing techniques. To date, a variety of techniques haven been proposed for 3D surface reconstruction using imagery collected by multi-platform photogrammetric systems. However, these approaches suffer from the lack of a well-established quality control procedure which evaluates the quality of reconstructed 3D surfaces independent of the utilized reconstruction technique. Hence, this paper aims to introduce a new quality assessment platform for the evaluation of the 3D surface reconstruction using photogrammetric data. This quality control procedure is performed while considering the quality of input data, processing procedures, and photo-realistic 3D surface modelling. The feasibility of the proposed quality control procedure is finally verified by quality assessment of the 3D surface reconstruction using images from different photogrammetric systems.

Fully automatic reconstruction of personalized 3D volumes of the proximal femur from 2D X-ray images.

Accurate preoperative planning is crucial for the outcome of total hip arthroplasty. Recently, 2D pelvic X-ray radiographs have been replaced by 3D CT. However, CT suffers from relatively high radiation dosage and cost. An alternative is to reconstruct a 3D patient-specific volume data from 2D X-ray images. In this paper, based on a fully automatic image segmentation algorithm, we propose a new control point-based 2D-3D registration approach for a deformable registration of a 3D volumetric template to a limited number of 2D calibrated X-ray images and show its application to personalized reconstruction of 3D volumes of the proximal femur. The 2D-3D registration is done with a hierarchical two-stage strategy: the scaled-rigid 2D-3D registration stage followed by a regularized deformable B-spline 2D-3D registration stage. In both stages, a set of control points with uniform spacing are placed over the domain of the 3D volumetric template first. The registration is then driven by computing updated positions of these control points with intensity-based 2D-2D image registrations of the input X-ray images with the associated digitally reconstructed radiographs, which allows computing the associated registration transformation at each stage. Evaluated on datasets of 44 patients, our method achieved an overall surface reconstruction accuracy of [Formula: see text] and an average Dice coefficient of [Formula: see text]. We further investigated the cortical bone region reconstruction accuracy, which is important for planning cementless total hip arthroplasty. An average cortical bone region Dice coefficient of [Formula: see text] and an inner cortical bone surface reconstruction accuracy of [Formula: see text] were found. In summary, we developed a new approach for reconstruction of 3D personalized volumes of the proximal femur from 2D X-ray images. Comprehensive experiments demonstrated the efficacy of the present approach.

Combining Multi-atlas Segmentation with Brain Surface Estimation.

Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this "segmentation to surface to parcellation" strategy has shown limitations in various situations. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly population.

A Trace and Retrace Scanning Combined Topography Reconstruction Strategy for Fast Scanning Atomic Force Microscopies

For nearly all atomic force microscopies (AFMs) utilized now, only the signals in trace scanning process are employed to reconstruct the sample surface topography, while the data of retrace scanning process are just used for adjustment. In this paper, a novel trace and retrace scanning combined (TRSC) topography reconstruction strategy, which successfully utilizes data from both trace and retrace processes, is proposed for AFMs to increase the surface reconstruction accuracy. Specifically, two reconstructed topography images are obtained, one is for trace scanning, and the other is for retrace scanning; the hysteresis distortion is successfully compensated with a data fusion-based postprocessing method; the alignment for trace and retrace images is further guaranteed with a feature point-based strategy; then the two images are combined together with confidence levels to reconstruct the final accurate topography image. Compared with the conventional scanning method, the proposed TRSC imaging algorithm provides much more accurate image for the sample surface, and it is especially valid for high-speed scanning tasks. Some simulation and experimental results are included to demonstrate the superior performance of the proposed imaging method.