Mesh

For those acquainted with mechanical design and reverse engineering, they can testify to the fact that the road to a new product design involves several steps. In reverse engineering, the summary of the entire process involves scanning, point cloud generation, meshing, computer-aided designing, prototyping and final production. This section covers a very crucial part of the process — Meshing or simply put, Mesh.

To put a simple definition, a mesh is a network that constitutes of cells and points.

Mesh generation is the practice of converting the given set of points into a consistent polygonal model that generates vertices, edges and faces that only meet at shared edges. It can have almost any shape in any size. Each cell of the mesh represents an individual solution, which when combined, results in a solution for the entire mesh.

 

mesh

Mesh is formed of facets which are connected to each other topologically. The topology is created using following entities:

  • Facet - A triangle connecting three data points
  • Edge - A line connecting two data points
  • Vertex - A data point
Mesh Property

Before we proceed to know the types of meshes, it is necessary to understand the various aspects that constitute a mesh. It is important to know the concept of a polygonal mesh.

A polygon mesh is a collection of vertices, edges and faces that defines the shape of a polyhedral object in 3D graphics and solid modeling. The faces usually consist of triangles, quadrilaterals or other simple polygons as that simplifies rendering. It may also be composed of more general concave polygons or polygons with holes.

Objects created with polygon meshes must store different types of elements. These include:

  • Vertex: A position (usually in 3D space) along with other information such as color, normal vector and texture coordinates
  • Edge: A connection between two vertices
  • Face: A closed set of edges, in which a triangle face has three edges, and a quad face has four edges
  • Surfaces: They are often called smoothing groups. Generally, surfaces are not required to group smooth regions

A polygon mesh may be represented in a variety of ways, using different methods to store the vertex, edge and face data. These include:

  • Face-vertex meshes
  • Winged edge meshes
  • Corner tables
  • Vertex-vertex meshes
Types of meshes

Meshes are commonly classified into two divisions, Surface mesh and Solid mesh. Let us go through each section one by one.

Surface Mesh: A surface mesh is a representation of each individual surface constituting a volume mesh. It consists of faces (triangles) and vertices. Depending on the pre-processing software package, feature curves may be included as well.

Generally, a surface mesh should not have free edges and the edges should not be shared by two triangles.

The surface should ideally contain the following qualities of triangle faces:

  • Equilateral sized triangles
  • No sharp angles/surface folds etc. within the triangle proximity sphere
  • Gradual variation in triangle size from one to the next

The surface mesh generation process should be considered carefully. It has a direct influence on the quality of the resulting volume mesh and the effort it takes to get to this step.

surface mesh

Solid Mesh: Solid mesh, also known as volume mesh, is a polygonal representation of the interior volume of an object. There are three different types of meshing models that can be used to generate a volume mesh from a well prepared surface mesh.

The three types of meshing models are as follows:

  • Tetrahedral - tetrahedral cell shape based core mesh
  • Polyhedral - polyhedral cell shape based core mesh
  • Trimmed - trimmed hexahedral cell shape based core mesh

Once the volume mesh has been built, it can be checked for errors and exported to other packages if desired.

solid mesh

Mesh type as per Grid structure

A grid is a cuboid that covers entire mesh under consideration. Grid mainly helps in fast neighbor manipulation for a seed point.

mesh grid

Meshes can be classified into two divisions from the grid perspective, namely Structured and Unstructured mesh. Let us have a look at each of these types.

Structured Mesh: Structured meshes are meshes which exhibits a well-known pattern in which the cells are arranged. As the cells are in a particular order, the topology of such mesh is regular. Such meshes enable easy identification of neighboring cells and points, because of their formation and structure. Structured meshes are applied over rectangular, elliptical, spherical coordinate systems, thus forming a regular grid. Structured meshes are often used in CFD.

structured mesh

Unstructured Mesh: Unstructured meshes, as the name suggests, are more general and can randomly form any geometry shape. Unlike structured meshes, the connectivity pattern is not fixed hence unstructured meshes do not follow a uniform pattern. However, unstructured meshes are more flexible. Unstructured meshes are generally used in complex mechanical engineering projects.

Unstructured Mesh

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Mesh - List of operations

Good cell quality of meshes translate into accurate results within optimum time after computation. But more often than not, we get a mesh output, which is far from accuracy. There are number of factors affecting a mesh, that might compromise with the final result. This chapter focuses on the various shortcomings of a mesh and their repair algorithms.

Mesh Decimation/Simplification

Mesh decimation/simplification is the method of reducing the number of elements used in a mesh while maintaining the overall shape, volume and boundaries preserved as much as possible. It is a type of algorithm that aims to transform a given mesh into another with fewer elements (faces, edges and vertices). The decimation process usually involves a set of user-defined quality criteria, that maintains specific properties of the original mesh as much as possible. This process reduces the complexity of a mesh.

Before Mesh Decimation

 

After Mesh Decimation

 

Mesh Hole-Filling

To analyze a mesh model, it must be complete. Often, some mesh models carry holes in them, which must be filled. The unseen areas of the model appear as holes, which are aesthetically unsatisfying and can be a hindrance to algorithms that expect a continuos mesh. The Fill Hole command fills the holes and gaps in the mesh.

Note – The Fill Hole command only works on triangulated mesh and not tetrahedral mesh

Mesh Before Hole Filling

 

Mesh After Hole Filling

 

Mesh Refinement

Certain situations arise which makes us concerned about the accuracy a model in certain areas. Such scenarios prompt us to have fine mesh in those areas to ensure accurate results. However, creating a surface mesh of the entire model with a fine mesh size may ask for unnecessary hours to analyze the fine mesh in those regions where the results are not as important to you. The answer to this issue is the usage of refinement points.

A refinement point identifies a region or volume of space in which a finer mesh has to be generated. Mesh refinement can be defined by identifying an absolute size for the local mesh. Mesh refinement ends up in creating more number of elements in the specified region of the model.

Before Mesh Refinement

 

After Mesh Refinement

 

Mesh Smoothing

Mesh smoothing is also known as mesh relaxation. Sometimes it is necessary to modify that mesh after a mesh generation. It is achieved either by changing the positions of the nodes or by removing the mesh altogether. Mesh smoothing results in the modification of mesh point positions, while the topology remains as it is.

Before Mesh Smoothing

 

After Mesh Smoothing

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Mesh Quality

The quality of a mesh plays a significant role in the accuracy and stability of the numerical computation. Regardless of the type of mesh used in your domain, checking the quality of your mesh is a must. The ‘good meshes’ are the ones that produce results with fairly acceptable level of accuracy, considering that all other inputs to the model are accurate. While evaluating whether the quality of the mesh is sufficient for the problem under modeling, it is important to consider attributes such as mesh element distribution, cell shape, smoothness, and flow-field dependency.

Element Distribution

It is known that meshes are made of elements (vertices, edges and faces). The extent, to which the noticeable features such as shear layers, separated regions, shock waves, boundary layers, and mixing zones are resolved, relies on the density and distribution of mesh elements. In certain cases, critical regions with poor resolution can dramatically affect results. For example, the prediction of separation due to an adverse pressure gradient depends heavily on the resolution of the boundary layer upstream of the point of separation.

Cell Quality

The quality of a cell has a crucial impact on the accuracy of the entire mesh. The quality of cell is analyzed by the virtue of three aspects: Orthogonal quality, Aspect ratio and Skewness.

Orthogonal Quality: An important indicator of mesh quality is an entity referred to as the orthogonal quality. The worst cells will have an orthogonal quality close to 0 and the best cells will have an orthogonal quality closer to 1.

Aspect Ratio: Aspect ratio is an important indicator of mesh quality. It is a measure of stretching of the cell. It is computed as the ratio of the maximum value to the minimum value of any of the following distances: the normal distances between the cell centroid and face centroids and the distances between the cell centroid and nodes.

Skewness: Skewness can be defined as the difference between the shape of the cell and the shape of an equilateral cell of equivalent volume. Highly skewed cells can decrease accuracy and destabilize the solution.

Smoothness

Smoothness redirects to truncation error which is the difference between the partial derivatives in the equations and their discrete approximations. Rapid changes in cell volume between adjacent cells results in larger truncation errors. Smoothness can be improved by refining the mesh based on the change in cell volume or the gradient of cell volume.

Flow-Field Dependency

The entire effects of resolution, smoothness, and cell shape on the accuracy and stability of the solution process is dependent upon the flow field being simulated. For example, skewed cells can be acceptable in benign flow regions, but they can be very damaging in regions with strong flow gradients.

Correct Mesh Size

Mesh size stands out as one of the most common problems to an equation. The bigger elements yield bad results. On the other hand, smaller elements make computing so long that it takes a long amount of time to get any result. One might never really know where exactly is the mesh size is on the scale.

It is important to consider chosen analysis for different mesh sizes. As smaller mesh means a significant amount of computing time, it is important to strike a balance between computing time and accuracy. Too coarse mesh leads to erroneous results. In places where big deformations/stresses/instabilities take place, reducing element sizes allow for greatly increased accuracy without great expense in computing time.

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Meshing Algorithms

In the previous session, we have learned what Mesh is and the various aspects upon which a mesh can be classified. Mesh generation requires expertise in the areas of meshing algorithms, geometric design, computational geometry, computational physics, numerical analysis, scientific visualization and software engineering to create a mesh tool.

Over the years, mesh generation technology has evolved shoulder to shoulder with increasing hardware capability. Even with the fully automatic mesh generators there are many cases where the solution time is less than the meshing time. Meshing can be used for wide array of applications, however the principal application of interest is the finite element method. Surface domains are divided into triangular or quadrilateral elements, while volume domain is divided mainly into tetrahedral or hexahedral elements. A meshing algorithm can ideally define the shape and distribution of the elements.

A key step of the finite element method for numerical computation is mesh generation algorithms. A given domain is to be partitioned it into simpler ‘elements’. There should be few elements, but some portions of the domain may need small elements so that the computation is more accurate there. All elements should be ‘well shaped’. Let us take a walkthrough of different meshing algorithms based of two common domains, namely quadrilateral/hexahedral mesh and triangle/tetrahedral mesh.

Algorithm methods for Quadrilateral or Hexahedral Mesh

Grid-Based Method

The grid based method involves the following steps:

  • A user defined grid is fitted on 2D & 3D object. It generates quad/ hex elements on the interior of the object.
  • Some patterns are defined for boundary elements followed by forming a boundary element by applying boundary intersection grid.
  • This results in the generation of quadrilateral mesh model.

Mesh Grid based method

 

Medial Axis Method

Medial axis method involves an initial decomposition of the volumes. The method involves few steps as given below:

  • Consider a 2D object with hole.
  • A maximal circle is rolled through the model and the centre of circle traces the medial object.
  • Medial object is used as a tool for automatically decomposing the model in to simple meshable region.
  • Series of templates for the region are formed by the medial axis method to fill the area with quad element.

Mesh Medial axis method

 

Plastering method

Plastering is the process in which elements are placed starting with the boundaries and advancing towards the centre of the volume. The steps of this method are as follows:

  • A 3D object is taken.
  • One hexahedral element is placed at boundary.
  • Individual hexahedral elements are projected towards the interior of the volume to form hexahedral meshing, row by row and element by element.
  • The process is repeated until mesh generation is completed.

Mesh Plastering method

 

Whisker Weaving Method

Whisker weaving is based on the concept of the spatial twist continuum (STC). The STC is the dual of the hexahedral mesh, represented by an arrangement of intersecting surfaces, which bisect hexahedral elements in each direction. The whisker weaving algorithm can be explained as in the following steps:

  • The first step is to construct the STC or dual of the hex mesh.
  • With a complete STC, the hex elements can then be fitted into the volume using the STC as a guide. The loops can be easily determined from an initial quad mesh of the surface.
  • Hexes are then formed inside the volume, once a valid topological representation of the twist planes is achieved. One hex is formed wherever three twist planes converge.

Mesh Whisker weaving method

 

Paving Method

The paving method has the following steps to generate a quadrilateral mesh:

  • Initially a 2D object is taken.
  • A node is inserted in the boundary and the boundary node is considered as loop.
  • A quadrilateral element is inserted and a row of elements is formed.
  • The row of element is placed around the boundary nodes.
  • Again this same procedure adopt for next rows.
  • Finally quad mesh model is formed.

Mesh Paving method

Mesh Paving method

 

Mapping Mesh Method

The Mapped method for quad mesh generation involves the following steps:

  • A 2D object is taken.
  • The 2D object is split into two parts.
  • Each part is either a simple 2D rectangular or a square object.
  • The simple shape object is unit meshed.
  • The unit meshed simple shape object is mapped in its original form and then joined back to form actual object.

Mapping mesh method

Mapping mesh method

 

Algorithm methods for Triangular and Tetrahedral Mesh

Quadtree Mesh Method

With the quadtree mesh method, square containing the geometric model are recursively subdivided until the desired resolution is reached. The steps for two dimensional quadtree decomposition of a model are as follows:

  • A 2D object is taken.
  • The 2D object is divided into rectangular parts.
  • A Detail tree of divided object is provided.
  • The object is eventually converted into triangle mesh.

 Quadtree mesh method

 

Delaunay Triangulation Method

A Delaunay triangulation for a set P of discrete points in the plane is a triangulation DT such that no points in P are inside the circum-circle of any triangles in DT. The steps of construction Delaunay triangulation are as follows:

  • The first step is to consider some coordinate points or nodes in space.
  • The condition of valid or invalid triangle is tested in every three points which finds some valid triangle to make a triangular element.
  • Finally a triangular mesh model is obtained.

Delaunay Triangulation maximizes the minimum angle of all the angle of triangle and it tends to avoid skinny triangles.

Mesh Delaunay Triangulation method

Mesh Delaunay Triangulation method

 

Advancing Front Method

Another very popular family of triangular and tetrahedral mesh generation algorithms is the advancing front method, or moving front method. The mesh generation process is explained as following steps:

  • A 2D object with a hole is taken.
  • An inner and outer boundary node is inserted. The node spacing is determined by the user.
  • An edge is inserted to connect the nodes.
  • To start the meshing process, an edge AB is selected and a perpendicular is drawn from the midpoint of AB to point C (where C is node spacing determined by the user) in order to make a triangular element.
  • After one element is generated, another edge is selected as AB and a point C is made, but if in case any other node lets point D within the defined radius, then ABC element is cancelled and instead, an element ABD is formed.
  • This process is repeated until mesh is generated.

Mesh Advancing Front method

 

Spatial Decomposition Method

The steps for spatial decomposition method are as follows:

  • Initially a 2D object is taken.
  • The 2D object is divided into minute parts till we get the refined triangular mesh.

Mesh Spatial Decomposition method

 

Sphere Packing Method

The sphere packing method follows the given steps:

  • Before constructing a mesh, the domain is filled with circles.
  • The circles are packed closely together, so that the gaps between them are surrounded by three or four tangent circles.
  • These circles are then used as a framework to construct the mesh, by placing mesh vertices at circle centers, points of tangency, and within each gap while using generated points. Eventually, the triangular mesh is generated.

Mesh Sphere Packing method

Mesh Sphere Packing method

 

 

 

 Source

Singh, Dr. Lokesh, (2015). A Review on Mesh Generation Algorithms. Retrieved from http://www.ijrame.com

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NPD/ID vocabulary

Bill of materials (BOM): A table containing a list of the components and the quantity of each required to produce an assembly.

BriefInstructions and requests provided to design team prior to the commencement of a project. 

Business analysis: The practice of identifying business needs and determining solutions to business problems.

Commercialization: The process of introducing a new product or production method into the market.

Concept design: An early phase of design process, where the broad outlines of function and form are articulated.

ErgonomicsApplication of principles that consider the effective, safe and comfortable use of design by humans.

Ideation: Idea generation or brainstorming.

Industrial design: The process of designing products used by millions of consumers around the world.

Market research: An organized effort to gather information about target markets or consumers.

New product development (NPD): The complete process which involves transformation of a market opportunity or product idea into a product available for sale.

New Product Introduction (NPI): New product introduction is the complete process of bringing a new product to market.

Patent: An exclusive right granted to an inventor by a sovereign authority, for a specified time period.

Pilot Run: An initial small production run produced as a check, prior to commencing full-scale production. 

Prototyping: An early sample, model, or release of a product built to test a concept or process or built to act as a commodity to be replicated or learned from.

SketchAn image that is quick to generate and does not contain complete detail.

S.W.O.TAnalysis framework for a company relative to its competitors, market, and industry: Strengths, Weaknesses, Opportunities & Threats.

Test marketing: An experiment conducted by companies to check the viability in the target market before full scale manufacture.

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Outsourcing Product Development

Outsourcing development activities has become an essential component of any successful business strategy these days. As the global competitive market is gradually changing, product based companies are going against established norms following the trend of outsourcing activities. This article discusses about the logic behind outsourcing product development, elaborates on its benefits and discusses the other aspects to be considered when outsourcing product development.

What is Outsourced Product Development?

The outsourcing of specific activities or all activities related to the development and maintenance of a product is known as Outsourced Product Development.

Outsourcing enables product companies to get access into an untapped product-building expertise and global talent pools available with service providers. This helps in exchange of technology and varied work-process.

Why Outsource Product Development?

Every decision making panel of an organization stumbles upon a vital question—whether to develop a product in-house or outsource the same to a third-party expertise.

The product development market is becoming more competitive and mature. As the competition intensifies, product companies are under immense pressure to periodically release new versions in the market. Being an intensive activity, product development requires a lot of attention. The top management can’t afford to put all the emphasis on one activity, while overlooking the other phases of product development. It will end up affecting the profitability of the company.

When to outsource product development?

A typical product lifecycle involves the following activities: product development, product reengineering and migration, product maintenance, product implementation, and product testing. A product based company can choose to outsource one or more of these activities or it can outsource the entire string related to a particular product to a service providing firm.

The question, however remains as to when outsource an activity. There are several factors to be taken into account before outsourcing product development. It varies with company to company. Sometimes it is even seasonal and based on current marketing trends. Some factor can be summarised like this:

  1. One needs to understand the purpose of a product before outsourcing and weigh the importance of such product in the market. The biggest thing is, if you have an area of expertise where you really are the best in the world and if it’s the key to your business; that is something that needs to be kept inside. Surely, the success of a product doesn’t depend on R&D alone, as without marketing, sales, distribution, the success won’t move an inch. So the decision making panel need to sort out the area of expertise that the product company lacks.
  1. There are several products which are solely made by the parent company. But they might need enhancements to act as a catalyst to make it more user-friendly or well operable. This happens more often in software industry. You might have a software product of your own, or you might own a mechanical product but you need specific software designed to assist it in its operation. That is when you should consider outsourcing your product development activity. There are various companies out there that might specialise in this area and that might turn out to be your destination.
  1. One of the most, or maybe the most important factor in case of outsourcing product development is— availability of expertise. Sometimes this weighs in far more than other factors. There are some areas which needs specific expertise. Most firms are not entirely self-sufficient hence they have to look out for someone who can get the job done. For example, developing complex CAD roofing software would need people with CAD and mathematical software development expertise. With the advent of new generation automobiles, vehicle manufacturers outsource voice recognition feature to the OPD (Outsourced Product Development) partner who is an expert in such technology.
  1. Generally, some established companies have enough capabilities to run the entire product development lifecycle themselves. However, even these same organizations opt for outsourcing one or two activities to outside vendors. On the contrary, start-ups usually outsource a big chunk of their product development activity. This might be due to inadequate manpower, expertise, capital to afford means and various other reasons.
  1. Pertaining to the last point, besides lack of expertise, inadequate manpower also plays a role in outsourcing of product development. A company would rather outsource PD, to make up for manpower, rather than headhunting themselves. Time consumption, employment policy are few factors responsible for such decisions.
  1. Geography is another point to consider, if there are ongoing discussions about OPD. Last decade has seen big label organizations outsourcing various activities, including R&D, outsourcing their activities to locations like China, India and Eastern Europe. This has a lot to do with the low cost attached to it. Such nations provide manpower and particular expertise in affordable costs, thereby saving the organizations a huge expenditure. The low-cost geography has actually changed the dynamics of product development in such way, that big names have opened up their own R&D centres, sales and distribution office, factories etc in such locations due to lower cost factors.
Benefits of outsourcing product development

OPD has many benefits. A product owner need not worry about the outcome or excess expenditure if the activity is in right hands. Correct and well planned outsourcing saves a product company time, expenditure on systems and manpower, legal hassles. The best part is exchange of domain knowledge between the product company and the OPD, something that ensures better output in the future.

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Parametric and Non-parametric modelling

Up until now, we believe our readers got a clear explanation of reverse engineering. Let us give walkthrough — Reverse engineering is the process of extracting design information after studying a physical product, with the intent to reproduce the product, or to create another object that can interact with it.

In the past, designers resorted to physical measurement of the product to redraw its geometry. Today, designers use 3D scanners to capture measurements. The scanned data is then imported to CAD where the design can be analyzed, processed, manipulated and refined. Two key aspects that fall in place when focusing on reverse engineering process are:

Parametric Model/Modeling

A parametric model captures all its information about the data within its parameters. All you need to know for predicting a future data value from the current state of the model is just its parameters.
The parameters are usually finite in dimensions. For a parametric model to predict new data, knowing just the parameters is enough. A parametric model is one where we assume the ‘shape’ of the data, and therefore only have to estimate the coefficients of the model.

Non-parametric Model/Modeling

A non parametric model can capture more subtle aspects of the data. It allows more information to pass from the current set of data that is attached to the model at the current state, to be able to predict any future data.
The parameters are usually said to be infinite in dimensions. Hence, it can express the characteristics in the data much better than parametric models. For a non parametric model, predicting future data is based on not just the parameters but also in the current state of data that has been observed. A non-parametric model is one where we do not assume the ‘shape’ of the data, and we have to estimate the most suitable form of the model, along with the coefficients.

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Path to Product Development

If you are an engineering professional, most likely you are aware of how a physical product comes to life. From the early days of sketching and blueprints, manufacturing of a commodity has come a long way. The modern methodology of creating a product has not only changed drastically, but it has become way more efficient and precise in its approach. Today’s engineer lives and thrives in the world of 3-dimensional models. Whatever masterpiece a designer has in his mind, he has the tools and system to give it life. And it is not just limited to inception of a new idea being turned to a product; it has made the art of reverse engineering being implemented more than ever.

So what are the factors that have revolutionized this craft?

It is the safe to say that with the invention of new tools, techniques and computer, the road to new product development has become more smooth, accurate and flexible. Although a professional can get deep into the subject matter, this article gives a brief overview of the product development from technical perspective.

The footsteps to a new product can be summarized in the following sequence.

 

path to product developmentTo put it in words, here is how the entire sequence goes:

  • Scanning: Whether you have an entirely new idea on your mind, or you want to base your idea on an already existing product; you need a reference. Your reference can be either technical manuals from the manufacturer or the physical product itself. The first step is to scan the product using 3D scanners. 3D scanning technology comes in many shapes and forms. Scanners capture and store the 3D information of the product. The scanned information gets stored in the form of closely spaced data points known as Point Cloud.
  • Point Cloud: A point cloud is a collection of data points defined by a given coordinates system. In a 3D coordinates system, for example, a point cloud may define the shape of some real or created physical system.
  • Mesh: Point clouds are used to create 3D meshes. A mesh is a network that constitutes of cells and points. Mesh generation involves point clouds to be connected to each other by the virtue of vertices, edges and faces that meet at shared edges. There are specific softwares for carrying of meshing function.
  • 3D Model: Once the meshed part is generated, it goes through required software applications to be transferred to Computer Aided Design (CAD) tools to get transformed into a proper 3D CAD model. 3D model is the stage where whole sorts of applications such as sewing, stitching, etc, are implemented to create a prototype.
  • Testing: A prototype goes through numerous tests in this phase, to check for limitations and possible calibrations if necessary. This is done to determine the optimum stage where the prototype can be turned to a product.
  • Product: This is where the entire process comes to an end. Once a prototype is evaluated and finalized, it is sent for production in order to introduce it to the market.

 This introductory part gives you a summary of product development and the related technical terms. In the next chapters, we will dive deep and go through all the mentioned stages, one by one.

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Point Cloud Operations

No output is always perfect no matter how much the technology has evolved. Even though point cloud generation has eased up manufacturing process, it comes with its own anomaly. Generally, a point cloud data is accompanied by Noises and Outliers.

Noises or Noisy data means the data information is contaminated by unwanted information; such unwanted information contributes to the impurity of the data while the underlying information still dominates. A noisy point cloud data can be filtered and the noise can be absolutely discarded to produce a much refined result.

If we carefully examine the image below, it illustrates a point cloud data with noises. The surface area is usually filled with extra features which can be eliminated.

 

Point Cloud Before noise redeuction

 

After carrying out Noise Reduction process, the image below illustrates the outcome, which a lot smoother data without any unwanted elements. There are many algorithms and processes for noise reduction.

 

Point Cloud After noise reduction

 

Outlier, on the contrary, is a type of data which is not totally meaningless, but might turn out to be of interest. Outlier is a data value that differs considerably from the main set of data. It is mostly different from the existing group. Unlike noises, outliers are not removed outright but rather, it is put under analysis sometimes.

The images below clearly portray what outliers are and how the point cloud data looks like once the outliers are removed.

 

Point Cloud With outliers

 

Point Cloud Without outliers

 

Point Cloud Decimation

We have learned how a point cloud data obtained comes with noise and outliers and the methods to reduce them to make the data more executable for meshing. Point cloud data undergoes several operations to treat the anomalies existing within. Two of the commonly used operations are Point Cloud Decimation and Point Cloud Registration.

A point cloud data consists of millions of small points, sometimes even more than what is necessary. Decimation is the process of discarding points from the data to improve performance and reduce usage of disk. Decimate point cloud command reduces the size of point clouds.

The following example shows how a point cloud underwent decimation to reduce the excess points.

Point Cloud Before decimation

 

Point Cloud After decimation

 

Point Cloud Registration

Scanning a commodity is not a one step process. A lot of time, scanning needs to be done separately from different angles to get views. Each of the acquired data view is called a dataset. Every dataset obtained from different views needs to be aligned together into a single point cloud data model, so that subsequent processing steps can be applied. The process of aligning various 3D point cloud data views into a complete point cloud model is known as registration. The purpose is to find the relative positions and orientations of the separately acquired views, such that the intersecting regions between them overlap perfectly.

Take a look at the example given below. The car door data sets have been merged to get a complete model.

 

Point Cloud before registration

 

Point Cloud After registration

 

 

 

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Point Clouds

Whether working on a renovation project or making an information data about an as-built situation, it is understandable that the amount of time and energy spent on analysis of the object/project in hand can be quite debilitating. Technical literatures over the years, has come up with several methods to make a precise approach. But inarguably, the most prominent method is the application of Point Clouds.

3D scanners gather point measurements from real-world objects or photos for a point cloud that can be translated to a 3D mesh or CAD model.

But what is a Point Cloud?

A common definition of point clouds would be — A point cloud is a collection of data points defined by a given coordinates system. In a 3D coordinates system, for example, a point cloud may define the shape of some real or created physical system.

Point clouds are used to create 3D meshes and other models used in 3D modeling for various fields including medical imaging, architecture, 3D printing, manufacturing, 3D gaming and various virtual reality (VR) applications. A point is identified by three coordinates that, correlate to a precise point in space relative to a point of origin, when taken together.
Point CloudThere are numerous ways of scanning an object or an area, with the help of laser scanners which vary based on project requirement. However, to give a generic overview of point cloud generation process, let us go through the following steps:

  1. The generation of a point cloud, and thus the visualization of the data points, is an essential step in the creation of a 3D scan. Hence, 3D laser scanners are the tools for the task. While taking a scan, the laser scanner records a huge number of data points returned from the surfaces in the area being scanned.
  1. Import the point cloud that the scanner creates into the point cloud modeling software. The software enables visualizing and modeling point cloud, which transforms it into a pixelated, digital version of the project. 
  1. Export the point cloud from the software and import it into the CAD/BIM system, where the data points can converted to 3D objects.
Different 3D point cloud file formats

Scanning a space or an object and bringing it into designated software lets us to further manipulate the scans, stitch them together which can be exported to be converted into a 3D model. Now there are numerous file formats for 3D modeling. Different scanners yield raw data in different formats. One needs different processing software for such files and each & every software has its own exporting capabilities. Most software systems are designed to receive large number of file formats and have flexible export options. This section will walk you through some known and commonly used file formats. Securing the data in these common formats enables the usage of different software for processing without having to approach a third party converter.

Common point cloud file formats

OBJ: It is a simple data format that only represents 3D geometry, color and texture. And this format has been adopted by a wide range of 3D graphics applications. It is commonly ASCII (American Standard Code for Information Interchange).

PLY: The full form of PLY is the polygon file format. PLY was built to store 3D data. It uses lists of nominally flat polygons to represent objects. The aim is to store a greater number of physical elements. This makes the file format capable of representing transparency, color, texture, coordinates and data confidence values. It is found in ASCII and binary versions.

PTS, PTX & XYZ: These three formats are quite common and are compatible with most BIM software. It conveys data in lines of text. They can be easily converted and manipulated.

PCG, RCS & RCP: These three formats were developed by Autodesk to specifically meet the demands of their software suite. RCS and RCP are relatively newer.

E57: E57 is a compact and widely used vendor-neutral file format and it can also be used to store images and data produced by laser scanners and other 3D imaging systems.

Challenges with point cloud data

The laser scanning procedure has catapulted the technology of product design to new heights. 3D data capturing system has come a long way and we can see where it’s headed. As more and more professionals and end users are using new devices, the scanner market is rising in a quick pace. But along with a positive market change, handling and controlling the data available becomes a key issue.

Five key challenges professionals working with point cloud face are:

  • Data Format: New devices out there in the market yields back data in a new form. Often, one needs to bring together data in different formats from different devices against a compatible software tool. This presents a not-so-easy situation
  • Data Size: With the advent of new devices, scanning has become cheaper with greater outputs. It is possible to scan huge assets from a single scan. This has resulted in the creation of tens of thousands of data points. A huge data of points can be challenging to handle and share between project partners.
  • Inter-operability: Integration between new technologies with the existing software can be quite arduous. Although, with careful investment of time and money, the goal can be achieved nonetheless.
  • Access: All the professionals involved in the entire lifecycle of a product can benefit from having access to point cloud data. But multiple datasets in multiple formats usually makes it more of a hassle.
  • Ownership: Who owns point cloud data? In the past, EPCs and the contractors who capture the data become custodians of the information.
  • Rendering: Different formats can result in rendering problems for point clouds.
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