An Optimized 3D Surface Reconstruction Method Using Spatial Kalman Filtering of Projected Line Patterns

This paper presents a 3D soft tissue surface reconstruction method based on improved compressed sensing and radial basis function interpolation for a small amount of uniform sampling data points on 3D surface. We adopt radial basis function interpolation to obtain the same amount of data points as to be reconstructed and propose an improved compressed sensing method to reconstruct 3D surface: we design a deterministic measurement matrix to signal observation, and then adopt the discrete cosine transform to the 3D coordinate sparse representation and use weak choose regularized orthogonal matching pursuit algorithm to reconstruct. Experimental results show that the proposed algorithm improves the resolution of the surface as well as the accuracy. The average maximum error is less than 0.9012 mm, which is smooth enough to provide accurate surface data model for virtual reality based surgery system.KeywordsCompressed sensingDeterministic measurement matrixRadial basis function interpolationSparse 3D discrete point

Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that the information detection results of the proposed method fit well with the actual situation, and the information detection efficiency is high.

To evaluate whether a computer vision model can utilize monocular endoscopic images of the middle ear to generate three-dimensional (3D) surface reconstructions that register to ground-truth (GT) imaging with clinically acceptable accuracy for surgical navigation. Eight temporal bone phantoms derived from the publicly available OpenEar data set were 3D-printed. Middle ear endoscopy was performed in each model using a standard monocular otologic endoscope, and a single endoscopic image captured of the mesotympanum. Images were processed with a monocular surface reconstruction model to generate predicted 3D surface reconstructions. Reconstructions were registered to GT meshes using natural anatomic landmarks. Surface reconstruction error (SRE) was quantified using the mean modified Hausdorff distance, and target registration error (TRE) was calculated using withheld test fiducials. Mean SRE across models was 0.58 ± 0.10mm, with 81.6% of points within 1mm of GT surfaces. Mean TRE was 0.96 ± 0.17mm. Simple 2D, monocular endoscopic images can be converted into high-fidelity 3D surface reconstructions of middle ear anatomy, which can be registered to ground-truth imaging with submillimeter registration accuracy. These findings establish the preclinical feasibility of monocular, endoscope-centric navigation for transcanal endoscopic ear surgery.

One of the challenging problems in geometric modeling and computer graphics is the construction of realistic human facial geometry. Such geometry are essential for a wide range of applications, such as 3D face recognition, virtual reality applications, facial expression simulation and computer based plastic surgery application. This paper addresses a method for the construction of 3D geometry of human faces based on the use of Elliptic Partial Differential Equations (PDE). Here the geometry corresponding to a human face is treated as a set of surface patches, whereby each surface patch is represented using four boundary curves in the 3-space that formulate the appropriate boundary conditions for the chosen PDE. These boundary curves are extracted automatically using 3D data of human faces obtained using a 3D scanner. The solution of the PDE generates a continuous single surface patch describing the geometry of the original scanned data. In this study, through a number of experimental verifications we have shown the efficiency of the PDE based method for 3D facial surface reconstruction using scan data. In addition to this, we also show that our approach provides an efficient way of facial representation using a small set of parameters that could be utilized for efficient facial data storage and verification purposes.

Abstract. In this paper, we propose the 3D surface reconstruction scheme using multi-view and multi-date Google Earth (GE) satellite images. Multi-view stereo matching (MVS) scheme is one of the methods for reconstructing dense 3D surface based on multi-view images and corresponding camera pose geometry. If many views are input, MVS can estimate the disparity (depth) by matching pixels. However, the common users are not always possible to obtain both multi-view satellite images and the camera geometry (such as Rational Polynomial Camera) in various earth regions. Instead, the GE provides multi-view and multi-date satellite images of earth regions. Therefore, the goal of the proposed method is to perform a 3D surface reconstruction using the GE satellite image. We suppose that the GE satellite image is a pinhole camera model, and the camera pose geometry is estimated using the perspective projection model (PPM) based structure from the motion (SfM) method. Then the 3D surface is reconstructed and fusion using the MVS method. However, the GE satellite image is a transformed pseudo-orthoimage for integration into the raster image. For this reason, the camera pose geometry is inaccurately computed in the SfM process. Thus, the high-rise structures in the reconstructed 3D surface are distorted (distorted hexahedral 3D space). Importantly, the satellite image is a weak PPM and it can express the orthographic projection model. Therefore, we compute 3D homography for transforming between distorted hexahedral space to orthographic cuboid space. Then, the distorted 3D surface is transformed using a projective reconstruction based on 3D homography. The transformed 3D surface has the correct shape in the orthographic projection model. The advantage of the proposed method is that the 3D surface of various earth regions is reconstructed using simply accessible GE satellite images. And the transformed 3D surface is reconstructed into orthographic projection model space, thus the orthoimage can be generated using projection.

This paper presents a novel method for 3D surface reconstruction that uses polarization and shading information from two views. The method relies on polarization data acquired using a standard digital camera and a linear polarizer. Fresnel theory is used to process the raw images and to obtain initial estimates of surface normals, assuming that the reflection type is diffuse. Based on this idea, the paper presents two novel contributions to the problem of surface reconstruction. The first is a technique to enhance the surface normal estimates by incorporating shading information into the method. This is done using robust statistics to estimate how the measured pixel brightnesses depend on the surface orientation. This gives an estimate of the object material reflectance function, which is used to refine the estimates of the surface normals. The second contribution is to use the refined estimates to establish correspondence between two views of an object. To do this, a set of patches are extracted from each view and are aligned by minimizing an energy functional based on the surface normal estimates and local topographic properties. The optimum alignment parameters for different patch pairs are then used to establish stereo correspondence. This process results in an unambiguous field of surface normals, which can be integrated to recover the surface depth. Our technique is most suited to smooth, non-metallic surfaces. It complements existing stereo algorithms since it does not require salient surface features to obtain correspondences. An extensive set of experiments, yielding reconstructed objects and reflectance functions, are presented and compared to ground truth.

Digital image-based 3D surface reconstruction is a streamlined and proper means of studying the features of the object being modelled. The generation of true 3D content is a very crucial step in any 3D system. A methodology to reconstruct a 3D surface of objects from a set of digital images is presented in this paper. It is simple, robust, and can be freely used for the construction of 3D surfaces from images. Digital images are taken as input to generate sparse and dense point clouds in 3D space from the detected and matched features. Poisson Surface, Ball Pivoting, and Alpha shape reconstruction algorithms have been used to reconstruct photo-realistic surfaces. Various parameters of these algorithms that are critical to the quality of reconstruction are identified and the effect of these parameters with varying values is studied. The results presented in this study will give readers an insight into the behaviour of various algorithmic parameters with computation time and fineness of reconstruction.

IntroductionThe American Society of Gastroenterology Endoscopy led Preservation and Incorporation of Valuable Endoscopic Innovations initiative has identified real time polyp diagnosis as one of the next major technology-driven changes in...

In 3D surface reconstruction, the sampling and quantization in the process of digitization limits the surface details obtained by the visual sensor. Obtaining a fine-grained 3D surface at lower cost remains a difficult problem. In our work, we have introduced a new method for enhancing 3D surface details reconstructed from single normal map without the support of hardware and reflection models. Using the new measurement of angle profile to infer microstructures hidden from existing surfaces. In addition, discrete geometry processing (DGP) can be used to further improve inference results. Numerous simulation results show that our method has achieved significant improvements on both subjective visual evaluation and objective quality metrics.

3D surface profilometry based on 2D S-Transform method with optimized window

Fire detection and 3D surface reconstruction based on stereoscopic pictures and probabilistic fuzzy logic

Chapter Three - Reconstruction of 3D Surfaces Using RBF Adjusted with PSO

In this paper we present a novel image-based 3D surface reconstruction technique that incorporates both reflectance and polarisation features into a variational framework. The proposed technique is suitable for single-image and multi-image (photopolarimetric stereo) analysis. It is especially suited for the difficult task of 3D reconstruction of rough metallic surfaces. An error functional consisting of several error terms related to the measured reflectance and polarisation properties is minimised in order to obtain a 3D reconstruction of the surface. We show that the combined approach strongly increases the accuracy of the surface reconstruction result, compared to techniques based on either reflectance or polarisation alone. We perform an evaluation of the algorithm with respect to single and multiple reflectance and polarisation images of the surface, relying on synthetic ground truth data. This evaluation also reveals which polarisation features should preferably be used in the context of 3D reconstruction of rough metallic surfaces. Furthermore, we report 3D reconstruction results for a raw forged iron surface, thus showing the applicability of our method in real-world scenarios, here in the domain of industrial quality inspection.

Recently it has been thought possible to characterise a pulmonary lesion by its vascular involvement. The complex vascular relationships are difficult to demonstrate by the "normal" technique of transverse CT sections. Spiral CT with 3D-surface reconstruction makes it possible to evaluate the vessels accurately and simulates the appearances one would have by looking into the thorax with a demonstration of all vessels lying within the pulmonary space examined by CT. Venous involvement was shown in every one of 18 malignant lesions but was also found in 4 out of 11 benign lesions, arterial involvement occurred in 14 out of 18 malignant and in 2 benign lesions. CT evidence of vascular involvement is in favour of a malignant tumour but is no absolute proof.

Structure-From-Motion for Earthwork Planning Khaled Nassar, Ebrahim A.Aly, Younghan Jung Pages 310-316 (2011 Proceedings of the 28th ISARC, Seoul, Korea, ISBN 978-89-954572-4-5, ISSN 2413-5844) Abstract: The reconstruction of surfaces from unorganized point clouds can be of great use in construction applications. Point clouds are mostly created by 3D scanners. Another technique for generating point clouds is the structure-from-motion technique, where the point cloud is generated from a sequence of images. This paper utilizes a surface reconstruction technique to model and quantify earthwork. The described technique can be used in preliminary planning, for project updates, estimating of earthwork quantities, as well as for embedded planning systems in construction equipment. The paper examines the use of structure-from-motion techniques in earthwork application and identifies limitations and, pros, and cons of this approach. Data from 23 earthwork excavation construction sites were collected and analyzed. 3D surface reconstructions were applied on the 23 sites during the construction phases and 3D surfaces were reconstructed and compared to the original land form. In addition, similar experiments were conducted with piles of earth. The results were analyzed to determine appropriate ranges of use for structure-from-motion surface reconstructions in earthwork applications. Results show that the technique is most suited for pit excavations with areas less than 2000 meter squared and with depths up to 5 meters. In addition, piles up to 10 meter highs and with base areas up to 300 meters were successfully reconstructed. The results of this paper should be of great importance to contractors willing to utilize this technology in everyday use. Keywords: Structure-from-motion, Construction, Earthwork, Image Processing, 3D Surface Reconstruction DOI: https://doi.org/10.22260/ISARC2011/0055 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley