Surface Model Of Object Research Articles

Research on Design Concept and Cultural Inheritance System of Tourism Cultural and Creative Products in the Context of Deep Learning

Abstract In this paper, the design process of cultural and creative product design is first constructed in the context of deep learning and the categories of cultural idea factors are planned. The training of the network for the image extraction classification task is accelerated due to the deep learning model’s fitting ability. Secondly, the 3D virtual intelligence algorithm is applied to use the point cloud as the basis for reconstruction in order to generate a highly reproducible object surface model. Finally, the K-means algorithm is employed to obtain the final extracted color, which constitutes the technical foundation of the product design system. The final results show that the color matching degree reaches 8.91, while the image design level reaches 9.24, which illustrates the effectiveness of the tourism cultural and creative product design system under deep learning.

Accuracy Verification of Surface Models of Architectural Objects from the iPad LiDAR in the Context of Photogrammetry Methods

The creation of accurate three-dimensional models has been radically simplified in recent years by developing photogrammetric methods. However, the photogrammetric procedure requires complex data processing and does not provide an immediate 3D model, so its use during field (in situ) surveys is infeasible. This paper presents the mapping of fragments of built structures at different scales (finest detail, garden sculpture, architectural interior, building facade) by using a LiDAR sensor from the Apple iPad Pro mobile device. The resulting iPad LiDAR and photogrammetric models were compared with reference models derived from laser scanning and point measurements. For small objects with complex geometries acquired by iPad LiDAR, up to 50% of points were unaligned with the reference models, which is much more than for photogrammetric models. This was primarily due to much less frequent sampling and, consequently, a sparser grid. This simplification of object surfaces is highly beneficial in the case of walls and building facades as it smooths out their surfaces. The application potential of the IPad LiDAR Pro is severely constrained by its range cap being 5 m, which greatly limits the size of objects that can be recorded, and excludes most buildings.

Utilizing polygon segmentation technique to extract and optimize light stripe centerline in line-structured laser 3D scanner

Light stripe centerline extraction is the basic and key procedure in line-structured laser three-dimensional (3D) scanner. Based on the fact that light stripe׳s contour is approximately parallel to its centerline, a novel contour polygon segmentation method is proposed for extracting and optimizing centerline. Different light stripe segments are identified in images by contour tracking, and then each of them is segmented into several parts using contour polygonization. Interior angle is defined to trim open light stripe polygon and contour to make sure that centerlines extracted from open light stripes do not include superfluous points. Taking advantage of polygon segmentation, piecewise polynomial fitting method and self-adaptive interpolating strategy are adopted to acquire smoother and evenly spaced centerline points. Simulated experiments show that the proposed method can calculate centerlines from images robustly with a 0.309 pixel average accuracy. Point clouds and surface models of different objects acquired by a line-structured laser scanner demonstrate that the proposed method can produce more complete and smoother 3D models compared to other classical methods. Processing time for the proposed method is approximately positive proportional to the number of pixels in image. Quantitative analysis of time used for each sub-procedure puts forward an improvement direction for the proposed method.

An Inexpensive Method for Measuring Deformation of Corrugated Cladding Using Close Range Photogrammetry

Close range photogrammetry is becoming more popular as the availability and affordability of photogrammetry software increases and could be used to great effect in a number of civil engineering applications. Close range photogrammetry can be used to create detailed Digital Surface Models (DSMs) of objects that have deformed under applied loads. In addition to capturing the nature of the deformation, the deflection of the object subjected to load can be inferred from the same DSM. This paper presents an inexpensive method for measuring the deformation and displacement of corrugated steel cladding using a single digital camera and low cost software programs. The development of this method has been documented in detail, including an assessment of its accuracy which was determined to be ± 0.1 mm for a 142 mm × 350 mm section of corrugated cladding. In addition, this paper demonstrates how DSMs are particularly useful for evaluating the performance of a numerical model. The DSM created using photogrammetry was compared directly to the deformed mesh predicted by a numerical model, enabling an expansive and quantitative validation of that numerical model. It was found that the current numerical model of corrugated cladding can predict the deformation of the cladding under an applied load of 800 N to within 96 % of that documented during experiments.

Three-Dimensional Structure Trajectory Guiding Scan and Points Cloud Registration

Three-dimensional visual scanning is an advanced non-contact measurement approach that can obtain a complete surface model of object. But, in the method, the local raster point cloud sets calculated must be registered to a whole. To reduce fluctuating errors and increase efficiency, we propose a new method that scanner can be moved around object guided by pre-planning trajectory of the robot. First, considering the structural parameters of the robot, the Inverse Kinematics Problem of each joint rotating function is derived, and the moving trajectory is simulated. Then, the relationship between the distances of controlled motion and data gotten by scanner is analyzed, a transform matrix which registered the local point cloud sets is deduced, the raster data can be normalized to the world coordinate by it. The experimental results show that error of registration is less than 0.09mm. The method is suited to measuring different targets in robot workspace. It can improve the efficiency and flexibility of visual measurement system.

Daylight Illumination Incident on Object Surface and Image Intensity Model

Daylight Illumination Incident on Object Surface and Image Intensity Model

Coarse-to-fine surface reconstruction from silhouettes and range data using mesh deformation

We present a coarse-to-fine surface reconstruction method based on mesh deformation to build watertight surface models of complex objects from their silhouettes and range data. The deformable mesh, which initially represents the object visual hull, is iteratively displaced towards the triangulated range surface using the line-of-sight information. Each iteration of the deformation algorithm involves smoothing and restructuring operations to regularize the surface evolution process. We define a non-shrinking and easy-to-compute smoothing operator that fairs the surface separately along its tangential and normal directions. The mesh restructuring operator, which is based on edge split, collapse and flip operations, enables the deformable mesh to adapt its shape to the object geometry without suffering from any geometrical distortions. By imposing appropriate minimum and maximum edge length constraints, the deformable mesh, hence the object surface, can be represented at increasing levels of detail. This coarse-to-fine strategy, that allows high resolution reconstructions even with deficient and irregularly sampled range data, not only provides robustness, but also significantly improves the computational efficiency of the deformation process. We demonstrate the performance of the proposed method on several real objects.

Photogrammetric reconstruction of free-form objects with curvilinear structures

The shapes of many natural or man-made objects have curve features. The images of such curves usually do not have sufficient distinctive features to apply conventional feature-based reconstruction algorithms. In this paper, we introduce a photogrammetric method for recovering free-form objects with curvilinear structures. Our method chooses to obtain the topology and geometry of a sparse 3D wireframe of the object first instead of directly recovering a surface or volume model. Surface patches covering the object are then constructed to interpolate the curves in this wireframe while satisfying certain heuristics such as minimal bending energy. The result is an object surface model with curvilinear structures from a sparse set of images. We can produce realistic texture-mapped renderings of the object model from arbitrary viewpoints. Reconstruction results on multiple real objects are presented to demonstrate the effectiveness of our approach.

3D scanning by multiple fan beam X‐ray sources and sensors

PurposeComputer tomography (CT) for 3D reconstruction entails a huge number of coplanar fan‐beam projections for each of a large number of 2D slice images, and excessive radiation intensities and dosages. For some applications its rate of throughput is also inadequate. A technique for overcoming these limitations is outlined.Design methodology/approach – A novel method to reconstruct 3D surface models of objects is presented, using, typically, ten, 2D projective images. These images are generated by relative motion between this set of objects and a set of ten fanbeam X‐ray sources and sensors, with their viewing axes suitably distributed in 2D angular space.Findings – The method entails a radiation dosage several orders of magnitude lower than CT, and requires far less computational power. Experimental results are given to illustrate the capability of the techniquePractical implications – The substantially lower cost of the method and, more particularly, its dramatically lower irradiation make it relevant to many applications precluded by current techniquesOriginality/value – The method can be used in many applications such as aircraft hold‐luggage screening, 3D industrial modelling and measurement, and it should also have important applications to medical diagnosis and surgery.

Free-form object segmentation and representation from registered range and color images

This paper describes a new technique to 3D surface segmentation and representation of free-form objects from registered range and color images. The proposed approach combines edge and region information to derive a surface description using algebraic implicit surfaces. This is done by propagating and blending Piecewise Hermite Interpolation Surfaces. The proposed technique, not only reduces the number of used patches, but also preserves surface-depth and orientation continuity. It can also be used to generate partial surface models for CAD-based vision and object recognition purposes. Realistic partial models are obtained by mapping texture extracted from a registered color image onto the generated surfaces. Experimental results show that the system can produce reliable surface segmentation as well as realistic 3D surface models of free-form objects.

Accurate integration of surface profile data with quantitative error analysis

In the process of surface profiling in experimental mechanics, multiple sets of measured, three-dimensional surface data oftentimes are obtained without precise knowledge of their relative position or orientation. In this work, automatic and accurate surface registration and merging algorithms are presented to integrate the individual subsets into a complete object surface model. The proposed method requires only overlapped regions between adjacent data sets when the measurement was taken and a rough initial estimate for the positions of each subset. Simulation results and experimental data are presented that demonstrate that the method is both accurate and efficient.

Volume Visualization in Radiation Treatment Planning

Radiation treatment planning (RTP), historically an image-intensive discipline and one of the first areas in which 3D information from imaging was clinically applied, has become even more critically dependent on accurate 3D definition of target and non-target structures in recent years with the advent of conformal radiation therapy. In addition to the interactive display of wireframe or shaded surface models of anatomic objects, proposed radiation beams, beam modifying devices, and calculated dose distributions, recently significant use has been made of direct visualization of relevant anatomy from image data. Dedicated systems are commercially available for the purpose of geometrically optimizing beam placement, implementing in virtual reality the functionality of standard radiation therapy simulators. Such "CT simulation" systems rely heavily on 3D visualization and on reprojection of image data to produce simulated radiographs for comparison with either diagnostic-quality radiographs made on a simulator or megavoltage images made using the therapeutic beams themselves. Although calculation and analysis of dose distributions is an important component of radiation treatment design, geometric targeting with optimization based on 3D anatomic information is frequently performed as a separate step independent of dose calculations.

Image processing in stereotactic planning: volume visualization and image registration

Radiation treatment planning (RTP), historically an image-intensive discipline and one of the first areas in which three-dimensional (3D) information from imaging was clinically applied, has become ever more critically dependent on accurate 3D identification of target and non-target organs with the advent of conformal radiation therapy, stereotactic radiotherapy, and radiosurgery. In addition to the interactive display of wireframe or shaded surface models of anatomical objects, proposed radiation beams, and calculated dose distributions, use may also be made of direct visualization of relevant anatomy from image data. Although anatomical analysis of dose distributions is an essential component of radiation treatment design which requires geometric definition (i.e., segmentation) of all volumes of interest, geometric targeting with optimization based on 3D anatomical information is frequently performed as a separate step independent of dose calculations. For this early step in the planning process, and certainly for diagnostic purposes, visualization without explicit segmentation may be a useful additional capability. Additionally, we suggest that direct visualization of high contrast targets such as arteriovenous malformations (AVM) from computerized tomography (CT) or magnetic resonance (MR) angiography may serve as a tool for target volume delineation. Frequently, information from multiple modalities or multiple scans of the same modality is important in planning a given case. In such instances, image registration may prove useful to allow synthesis of information from multiple sources into a single consistent coordinate frame. Numerous image registration methods are available, each with characteristic strengths and weaknesses.

Direct surface parameter estimation using structured light: a predictor-corrector based approach

The projection of structured light is a technique frequently used in computer vision to determine the surface structure of scene objects. In this work, higher level features are extracted from the images and used for a direct estimation of second-order object surface model parameters. In particular, a class of cylinders is emphasized, due to an underlying application involving the inspection of stored drums containing mixed waste. The strategy is based upon a predictor-corrector approach which utilizes an initial estimate for the surface parameters, followed by iterative parameter refinement. A predicted passive image is generated using the current surface parameter estimates and significant features are extracted and compared with those in the true passive image. The estimated surface parameters are corrected based upon feature disparities. In computer simulations and laboratory experiments using real image data, the algorithm was found to converge quickly and to yield accurate results.

Direct surface parameter estimation using structured light: a predictor-corrector based approach

The projection of structured light is a technique frequently used in computer vision to determine the surface structure of scene objects. In this work, higher level features are extracted from the images and used for a direct estimation of second-order object surface model parameters. In particular, a class of cylinders is emphasized, due to an underlying application involving the inspection of stored drums containing mixed waste. The strategy is based upon a predictor-corrector approach which utilizes an initial estimate for the surface parameters, followed by iterative parameter refinement. A predicted passive image is generated using the current surface parameter estimates and significant features are extracted and compared with those in the true passive image. The estimated surface parameters are corrected based upon feature disparities. In computer simulations and laboratory experiments using real image data, the algorithm was found to converge quickly and to yield accurate results.

A computer vision approach to air flow analysis

This paper describes a computer vision system for analysing air flows. The main idea of the system is to use a trace gas to visualize the air flow and to determine the air flow by analysing the trace gas and its movement via computer vision and neural network techniques. The analysis process is based on three major stages: first, an image pair series is taken from the traced air flow; second, the 3-D shapes of trace gas objects at each image pair instant are recovered; and third, the air flow velocity field is calculated via the Self-Organizing Map from the recovered 3-D surface models of the trace gas objects. The system can be used to measure air flow profiles, to determine air flow velocities and volume fluxes, and to estimate how small (e.g. dust) particles move within the flow.

A general surface approach to the integration of a set of range views

This paper presents a new and general solution to the problem of range view integration. The integration problem consists in computing a connected surface model from a set of registered range images acquired from different viewpoints. The proposed method does not impose constraints on the topology of the observed surfaces, the position of the viewpoints, or the number of views that can be merged. The integrated surface model is piecewise estimated by a set of triangulations modeling each canonical subset of the Venn diagram of the set of range views. The connection of these local models by constrained Delaunay triangulations yields g non-redundant surface triangulation describing all surface elements sampled by the set of range views. Experimental results show that the integration technique can be used to build connected surface models of free-form objects. No integrated models built from objects of such complexity have yet been reported in the literature, It is assumed that accurate range views are available and that frame transformations between all pairs of views can be reliably computed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Registering multiview range data to create 3D computer objects

Concerns the problem of range image registration for the purpose of building surface models of 3D objects. The registration task involves finding the translation and rotation parameters which properly align overlapping views of the object so as to reconstruct from these partial surfaces, an integrated surface representation of the object. The registration task is expressed as an optimization problem. We define a function which measures the quality of the alignment between the partial surfaces contained in two range images as produced by a set of motion parameters. This function computes a sum of Euclidean distances from control points on one surfaces to corresponding points on the other. The strength of this approach is in the method used to determine point correspondences. It reverses the rangefinder calibration process, resulting in equations which can be used to directly compute the location of a point in a range image corresponding to an arbitrary point in 3D space. A stochastic optimization technique, very fast simulated reannealing (VFSR), is used to minimize the cost function. Dual-view registration experiments yielded excellent results in very reasonable time. A multiview registration experiment took a long time. A complete surface model was then constructed from the integration of multiple partial views. The effectiveness with which registration of range images can be accomplished makes this method attractive for many practical applications where surface models of 3D objects must be constructed. >