I am dealing with a reverse-engineering problem regarding road geometry and estimation of design conditions.
Suppose you have a set of points obtained from the measurement of positions of a road. This road has straight sections as well as curve sections. Straight sections are, of course, represented by lines, and curves are represented by circles of unknown center and radius. There are, as well, transition sections, which may be clothoids / Euler spirals or any other usual track transition curve. A representation of the track may look like this:
We know in advance that the road / track was designed taking this transition + circle + transition principle into account for every curve, yet we only have the measurement points, and the goal is to find the parameters describing every curve on the track, this is, the transition parameters as well as the circle's center and radius.
I have written some code using a nonlinear optimization algorithm, where a user can select start and end points and fit a circle that to the arc section between them, as it shows in next figure:
However, I don't find a suitable way to take the transition into account. After giving it some thought I came to think that this s because, given a set of discrete points -with their measurement error- representing a full curve, it is not entirely clear where to consider it "begins" and where it "ends" and, moreover, it is less clear where to consider the transition, the proper circle and the exit transition "begin" and "end".
Is there any work on this subject which I may have missed? is there a proper way to fit the whole transition + curve + transition structure into the set of points?
As far as I know, there's no method to fit a sequence clothoid1-circle-clothoid2 into a given set of points.
Basic facts are that two points define a straight, and three points define a unique circle.
The clothoid is far more complex, because you need: The parameter A, the final radius Rf, an initial point px,py, the radius Ri at that point, and the tangent T (angle with X-axis) at that point.
These are 5 data you may use to find the solution.
Due to clothoid coords are calculated by expanded Fresnel integrals (see https://math.stackexchange.com/a/3359006/688039 a little explanation), and then apply a translation & rotation, there's no an easy way to fit this spiral into a set of given points.
When I've had to deal with your issue, what I've done is:
Calculate the radius for triplets of consecutive points: p1p2p3, p2p3p4, p3p4p5, etc
Observe the sequence of radius. Similar values mean a circle, increasing/decreasing values mean a clothoid; Big values would mean a straight.
For each basic element (line, circle) find the most probably characteristics (angles, vertices, radius) by hand or by some regression method. Many times the common sense is the best.
For a spiral you may start with aproximated values, taken from the adjacent elements. These values may very well be the initial angle and point, and the initial and final radius. Then you need to iterate, playing with Fresnel and 'space change' until you find a "good" parameter A. Then repeat with small differences in the other values, those you took from adjacents.
Make the changes you consider as good. For example, many values (A, radius) use to be integers, without decimals, just because it was easier for the designer to type.
If you can make a small applet to do these steps then it's enough. Using a typical roads software helps, but doesn't avoid you the iteration process.
If the points are dense compared to the effective radii of curvature, estimate the local curvature by least square fitting of a circle on a small number of points, taking into account that the curvature is most of the time zero.
You will obtain a plot with constant values and ramps that connect them. You can use an estimate of the slope at the inflection points to figure out the transition points.
Related
I'm using a polygonal chain to approximate a curve. I want to approximate the average of a function of curvature of all points that lie on the curve. One function of curvature that I need is, for example, the square of curvature.
I can get near that by choosing some points on the chain, calculating the curvature in those points, applying the function on it (for example squaring it), and then averaging the calculated values.
I need both accuracy and speed. I appreciate both — fast, but approximate; as well as accurate, but slow solutions. I'm working in Java, but the answer doesn't need to be written in Java — it doesn't even need to contain any code at all.
Polygonal chain with uniform segment length
If the polygonal chain's segments all have equal length, I can just calculate the curvature in the vertices and then average that. I see two ways to get the curvature in a vertex.
One way is to get the circle that goes through the selected vertex, the vertex before it, and the one after it. The curvature is then 1/radius of the circle.
The other way is to calculate the external angle (in radians) of the two segments connected at the selected vertex and then divide its absolute value by the length of a segment. In the following image, φ marks the external angle:
I am not sure if this method is correct, as I haven't mathematically derived it, but I've noticed through experimentation that it gives similar results to the above method.
Polygonal chain with non-uniform segment length
Unfortunately, though, there's no guarantee that the segments have uniform length.
If I try using the first of the above methods, vertices connected to longer segments give lower curvatures, even if they are visibly sharper. I tried substituting previous and next vertices with a point x units before the selected vertex and a point x units after it. I don't know what to set the x constant to, to get accurate results. All the values I've tried seemed to give inaccurate results.
If I try using the second method, I don't know what length to divide the angle by. If I don't divide by anything at all, I actually get pretty good results for comparing two curves and determining which one is curvier, but I need to be able to determine the actual curvature in a point.
With both of these methods there's also the problem that parts with shorter segments (where points are denser) will affect the average more.
Another possible solution would be to ignore the vertices and instead use an array of points on the chain that are evenly spaced, treat them as a new polygonal chain (connect the points with straight lines), and then calculate curvatures on this new chain instead, using one of the methods I mentioned under the header titled "Polygonal chain with uniform segment length".
Finding such an array of points is not trivial, though, because I have to choose a segment length, and only after producing the points, I can see if the length of the resulting chain is divisible by the chosen segment length.
If you aren't short on space, the last solution you mentioned would be the best, because the "sphere" approximation, as you've perhaps realized, would give awful results in more extreme cases, especially if the curvature is large or changes sign quickly.
There are many ways to do interpolations, the simplest being quadratic and cubic splines. However if you have more pre-processing time, Lagrange polynomials produce very good results: https://en.wikipedia.org/wiki/Lagrange_polynomial.
Side note on your angle division method, consider this diagram:
(From simple geometry the inside angle there is also theta)
For a << l. So the curvature:
So your approximation is in fact correct for small curvatures.
An alternative is to use a local parabola approximation to estimate the curvature. Basically, to estimate the curvature at point P(i), you take P(i-1), P(i) and P(i+1) and construct a parabola from these 3 points. Then, you compute the curvature at P(i) from the parabola. Remember to use chord-length (or centripetal) parametrization when constructing the parabola.
Bezier curve is a parametric curve, meaning that there is a paramater t at which one can evaluate the polynomials in order to find out the positions of points laying on the curve.
Polynomials for some common cases can be found at en.wikipedia.org/wiki/B%C3%A9zier_curve#Specific_cases
To draw a Bezier curve on screen, one could evaluate the polynomials from 0 to 1 at ever increasing t by tiny little steps. However, that would very wasteful because, in general, the parameter "space" does not correspond to screen "space", i.e. several little steps may fall onto just one pixel.
My question is: how to find the smallest step which increases Cartesian distance from previous point at least by 1 pixel?
To put it in other way: I would like to draw a Bezier curve on screen. How to choose the (uniform) step by which t should grow so that I never draw at one pixel more the once? I don't mind the "holes" when the t grows too quickly, I just don't want to redraw already drawn pixels.
Edit
By "how to find" I mean O(1). Yes, I could use De Casteljau's algorithm but I was hoping there is a way to "guess" the optimal t step quickly.
The comment above (jozxyqk) gives you a hint. I would give it a try with a recursive binary division of the spline drawing.
Lets say you start with a coarse resolution of the parameter space (delta_t = 0.1), that gives you 11 points on the spline curve s , s(t=0), s(t=0.1), ..., s(t=0.9), s(t=1).
Calculate the distance between s(t_i) and s(t_i+1). If it is >1 than make a binary subdivision between those two points. And so on...
But honestly, I guess it is faster to calculate all points a a higher resolution without any recursive loops or subdivisions. Especially if you are using multithread programming.
Given an ordered list of points, I want to draw a smooth curve that passes through all of them. Each part of the curve can either be horizontal, vertical, or an arc with given radius r (all arcs will have the same radius). The transitions should be smooth, i.e., the heading at the end of one part should be the same as the heading at the beginning of the next part. There can be any number of arcs or straight line segments between any two consecutive input points.
It's sort of like a train track that should run orthogonally or along sections with fixed curvature.
Is there a good algorithm to construct such a curve? (or, in cases where such a line is not possible, I would like to know that.)
I looked into Bezier curves, but that seems like overkill and I couldn't find a good way to enforce my constraints.
What you are asking for above implies to me that you seek tangent continuity of your curve across points (similar to a spline with tangent continuity at knots). The train track analogy at least conveys this requirement. Given the strict limitations of straight lines, and fixed radius circular arcs I am fairly certain that you will not be able to do this. Why not consider a spline interpolation of your points if you require such smoothness instead?
To see why consider the following image:
Consider replacing the line segment between B and C with a circular arc. You can do it to make the join continuous, but to make it tangent continuous, you would need a great deal of good fortune as there is only one circle that is tangent continuous to the line segment AB that also touches point C. The chances of that circle having tangent at C matching the tangent of line CD is remote. It is possible that your data will line up like this but you cannot rely on it.
If I have misunderstood your question please let me know and I will adjust the answer.
I am rewriting my ray tracer and just trying to better understand certain aspects of it.
I seem to have down pat the issue regarding normals and how you should multiply them by the inverse of the transpose of a transformation matrix.
What I'm confused about is when I should be normalizing my direction vectors?
I'm following a certain book and sometimes it'll explicitly state to Normalize my vector and other cases it doesn't and I find out that I needed to.
Normalized vector is in the same direction with just unit length 1? So I'm unclear when it is necessary?
Thanks
You never need to normalize a vector unless you are working with the angles between vectors, or unless you are rotating a vector.
That's it.
In the former case, all of your trig functions require your vectors to land on a unit circle, which means the vectors are normalized. In the latter case, you are dividing out the magnitude, rotating the vector, making sure it stays a unit, and then multiplying the magnitude back in. Normalization just goes with the territory.
If someone tells you that coordinate system are defined by n unit vectors, know that i-hat, j-hat, k-hat, and so on can be any arbitrary vector(s) of any length and direction, so long as none of them are parallel. This is the heart of affine transformations.
If someone tries to tell you that the dot product requires normalized vectors, shake your head and smile. The dot product only needs normalized vectors when you are using it to get the angle between two vectors.
But doesn't normalization make the math "simpler"?
Not really -- It adds a magnitude computation and a division. Numbers between 0..1 are no different than numbers between 0..x.
Having said that, you sometimes normalize in order to play well with others. But if you find yourself normalizing vectors as a matter of principle before calling methods, consider using a flag attached to the vector to save yourself a step. Mathematically, it is unimportant, but practically, it can make a huge difference in performance.
So again... it's all about rotating a vector or measuring its angle against another vector. If you aren't doing that, don't waste cycles.
tl;dr: Normalized vectors simplify your math. They also reduce the number of very hard to diagnose visual artifacts in your images.
Normalized vector is in the same direction with just unit length 1? So
I'm unclear when it is necessary?
You almost always want all vectors in a ray tracer to be normalized.
The simplest example is that of the intersection test: where does a bouncing ray hit another object.
Consider a ray where:
p(t) = p_0 + v * t
In this case, a point anywhere along that ray p(t) is defined as an offset from the original point p_0 and an offset along a particular direction v. For every increment of parameter t, the resulting p(t) will move another increment of length equal to the length of the vector v.
Remember, you know p_0 and v. When you are trying to find the point where this ray next hits another object, you have to solve for that t. It is obviously more convenient, if not always obviously necessary, to use normalized vector vs in that representation.
However, that same vector v is used in lighting calculations. Imagine that we have another direction vector u that points towards a lighting source. For the purpose of a very simple shading model, we can define the light at a particular point to be the dot product between those two vectors:
L(p) = v * u
Admittedly, this is a very uninteresting reflection model but it captures the high points of the discussion. A spot on a surface is bright if reflection points towards the light and dim if not.
Now, remember that another way of writing this dot product is the product of the magnitudes of the vectors times the cosine of the angle between them:
L(p) = ||v|| ||u|| cos(theta)
If u and v are of unit length (normalized), then the equation will evaluate to be proportional to the angle between the two vectors. However, if v is not of unit length, say because you didn't bother to normalize after reflecting the vector in the ray model above, now your lighting model has a problem. Spots on the surface using a larger v will be much brighter than spots that do not.
It is necessary to normalize a direction vector whenever you use it in some math that is influenced by its length.
The prime example is the dot product, which is used in most lighting equations. You also sometimes need to normalize vectors that you use in lighting calculations, even if you believe that they are normal.
For example, when using an interpolated normal on a triangle. Common sense tells you that since the normals at the vertices are normal, the vectors you get by interpolating are too. So much for common sense... the truth is that they will be shorter unless they incidentially all point into the same direction. Which means that you will shade the triangle too dark (to make matters worse, the effect is more pronounced the closer the light source gets to the surface, which is a... very funny result).
Another example where a vector might or might not be normalized is the cross product, depending on what you are doing. For example, when using the two cross products to build an orthonormal base, then you must at least normalize once (though if you do it naively, you end up doing it more often).
If you only care about the direction of the resulting "up vector", or about the sign, you don't need to normalize.
I'll answer the opposite question. When do you NOT need to normalize? Almost all calculations related to lighting require unit vectors - the dot product then gives you the cosine of the angle between vectors which is really useful. Some equations can still cope but become more complex (essentially doing the normalization in the equation) That leaves mostly intersection tests.
Equations for many intersection tests can be simplified if you have unit vectors. Some do not require it - for example if you have a plane equation (with a unit normal) you can find the ray-plane intersection without normalizing the ray direction vector. The distance will be in terms of the ray direction vectors length. This might be OK if all you want is to intersect a bunch of those planes (the relative distances will all be correct). But as soon as you want to compare with a different distance - calculated using the normalized ray direction - the distance values will not compare properly.
You might think about normalizing a direction vector AFTER doing some work that does not require it - maybe you have an acceleration structure that can be traversed without a normalized vector. But that isn't relevant either because eventually the ray will hit something and you're going to want to do a lighting/shading calculation with it. So you may as well normalize them from the start...
In other words, any specific calculation may not require a normalized direction vector, but a given direction vector will almost certainly need to be normalized at some point in the process.
Vectors are used to store two conceptually different elements: points in space and directions:
If you are storing a point in space (for example the position of the camera, the origin of the ray, the vertices of triangles) you don't want to normalize, because you would be modifying the value of the vector, and losing the specific position.
If you are storing a direction (for example the camera up, the ray direction, the object normals) you want to normalize, because in this case you are interested not in the specific value of the point, but on the direction it represents, so you don't need the magnitude. Normalization is useful in this case because it simplifies some operations, such as calculating the cosine of two vectors, something that can be done with a dot product if both are normalized.
What kind of algorithms would generate random "goo balls" like those in World of Goo. I'm using Proccesing, but any generic algorithm would do.
I guess it boils down to how to "randomly" make balls that are kind of round, but not perfectly round, and still looking realistic?
Thanks in advance!
The thing that makes objects realistic in World of Goo is not their shape, but the fact that the behavior of objects is a (more or less) realistic simulation of 2D physics, especially
bending, stretching, compressing (elastic deformation)
breaking due to stress
and all of the above with proper simulation of dynamics, with no perceivable shortcuts
So, try to make the behavior of your objects realistic and that will make them look (feel) realistic.
Not sure if this is what you're looking for since I can't look at that site from work. :)
A circle is just a special case of an ellipse, where the major and minor axes are equal. A squished ball shape is an ellipse where one of the axes is longer than the other. You can generate different lengths for the axes and rotate the ellipse around to get these kinds of irregular shapes.
Maybe Metaballs (wiki) are something to start from.. but I'm not sure.
Otherwise I would suggest a particle approach in which a ball is composed by many particles that stick together, giving an irregularity (mind that this needs a minimal physical engine to handle the spring body that keeps all particles together).
As Unreason said, World of Goo is not so much about shape, but physics simulation.
But an easy way to create ball-like irregular shapes could be to start with n vertices (points) V_1, V_2 ... V_n on a circle and apply some random deformation to it. There are many ways to do that, going from simply moving around some single vertices to complex physical simulations.
Some ideas:
1) Chose a random vertex V_i, chose a random vector T, apply that vector as a translation (movement) to V_i, apply T to all other vertices V_j, too, but scaled down depending on the "distance" from V_i (where distance could be the absolute differenece between j and i, or the actual geometric distance of V_j to V_i). For the scaling factor you could use any function f that is 1 for f(0) and decreasing for increasing distances (basically a radial basis function).
for each V_j
V_j = scalingFactor(distance(V_i, V_j)) * translationVector + V_j
2) You move V_i as in 1, but now you simulate springlike connections between all neigbouring vertices and iteratively move all vertices based on the forces created by stretched springs.
3) For more round shapes you can do 1) or 2) on the control points of a B-spline curve.
Beware of self-intersections when you move vertices too much.
Just some rough ideas, not tested...