I understand the concept of LOD but I am trying to find out the negative side of it and I see no reference to that from Googling around. The only pro I keep coming across is that it improves performance by omitting details when an object is far and displaying better graphics when the object is near.
Seriously that is the only pro and zero con? Please advice. Tnks.
There are several kinds of LOD based on camera distance. Geometric, animation, texture, and shading variations are the most common (there are also LOD changes that can occur based on image size and, for gaming, hardware capabilities and/or frame rate considerations).
At far distances, models can change tessellation or be replaced by simpler models. Animated details (say, fingers) may simplify or disappear. Textures may move to simpler textures, bump maps vanish, spec/diffuse maps combines, etc. And shaders may also swap-put to reduce the number of texture inputs or calculation (though this is less common and may be less profitable, since when objects are far away they already fill fewer pixels -- but it's important for screen-filling entities like, say, a mountain).
The upsides are that your game/app will have to render less data, and in some cases, the LOD down-rezzed model may actually look better when far away than the more-complex model (usually because the more detailed model will exhibit aliasing when far away, but the simpler one can be tuned for that distance). This frees-up resources for the nearer models that you probably care about, and lets you render overall larger scenes -- you might only be able to render three spaceships at a time at full-res, but hundreds if you use LODs.
The downsides are pretty obvious: you need to support asset swapping, which can mean both the real-time selection of different assets and switching them but also the management (at times of having both models in your memory pipeline (one to discard, one to load)); and those models don't come from the air, someone needs to create them. Finally, and this is really tricky for PC apps, less so for more stable platforms like console gaming: HOW DO YOU MEASURE the rendering benefit? What's the best point to flip from version A of a model to B, and B to C, etc? Often LODs are made based on some pretty hand-wavy specifications from an engineer or even a producer or an art director, based on hunches. Good measurement is important.
LOD has a variety of frameworks. What you are describing fits a distance-based framework.
One possible con is that you will have inaccuracies when you choose an arbitrary point within the object for every distance calculation. This will cause popping effects at times since the viewpoint can change depending on orientation.
Related
I am trying to build a system that on providing an image of a car can assess the damage percentage of it and also find out which parts are damaged in the car.
Is there any possible way to do this using Python and open-cv or tensorflow ?
The GitHub repositories I found that were relevant to my work are these
https://github.com/VakhoQ/damage-car-detector/tree/master/DamageCarDetector
https://github.com/neokt/car-damage-detective
But what they provide is a qualitative output( like they say the car damage is high or low), I wanted to print out a quantitative output( percentage of damage ) along with the individual part names which are damaged
Is this possible ?
If so please help me out.
Thank you.
To extend the good answers given by #yves-daoust: It is not a trivial task and you should not try to do it at once with one single approach.
You should question yourself how a human with a comparable task, i.e. say an expert who reviews these cars after a leasing contract, proceeds with this. Then you have to formulate requirements and also restrictions for your system.
For instance, an expert first checks for any visual occurences and rates these, then they may check technical issues which may well be hidden from optical sensors (i.e. if the car is drivable, driving a round and estimate if the engine is running smoothly, the steering geometry is aligned (i.e. if the car manages to stay in line), if there are any minor vibrations which should not be there and so on) and they may also apply force (trying to manually shake the wheels to check if the bearings are ok).
If you define your measurement system as restricted to just a normal camera sensor, you are somewhat limited within to what extend your system is able to deliver.
If you just want to spot cosmetic damages, i.e. classification of scratches in paint and rims, I'd say a state of the art machine vision application should be able to help you to some extent:
First you'd need to detect the scratches. Bear in mind that visibility of scratches, especially in the field with changing conditions (sunlight) may be a very hard to impossible task for a cheap sensor. I.e. to cope with reflections a system might need to make use of polarizing filters, special effect paints may interfere with your optical system in a way you are not able to spot anything.
Secondly, after you detect the position and dimension of these scratches in the camera coordinates, you need to transform them into real world coordinates for getting to know the real dimensions of these scratches. It would also be of great use to know the exact location of the scratch on the car (which would require a digital twin of the car - which is not to be trivially done anymore).
After determining the extent of the scratch and its position on the car, you need to apply a cost model. Because some car parts are easily fixable, say a scratch in the bumper, just respray the bumper, but scratch in the C-Pillar easily is a repaint for the whole back quarter if it should not be noticeable anymore.
Same goes with bigger scratches / cracks: The optical detection model needs to be able to distinguish between scratches and cracks (which is very hard to do, just by looking at it) and then the cost model can infer the cost i.e. if a bumper needs just respray or needs complete replacement (because it is cracked and not just scratched). This cost model may seem to be easy but bear in mind this needs to be adopted to every car you "scan". Because one cheap damage for the one car body might be a very hard to fix damage for a different car body. I'd say this might even be harder than to spot the inital scratches because you'd need to obtain the construction plans/repair part lists (the repair handbooks / repair part lists are mostly accessible if you are a registered mechanic but they might cost licensing fees) of any vehicle you want to quote.
You see, this is a very complex problem which is composed of multiple hard sub-problems. The easiest or probably the best way to do this would be to do a bottom up approach, i.e. starting with a simple "scratch detector" which just spots scratches in paint. Then go from there and you easily see what is possible and what is not
I'm trying to create my own equalizer. I want to implement 10 IIR bandpass filters. I know the equations to calculate those but I read that for higher center frequencies (above 6000Hz) they should be calculated differently. Of course I have no idea how (and why). Or maybe it's all lies and I don't need other coefficients?
Source: http://cache.freescale.com/files/dsp/doc/app_note/AN2110.pdf
You didn't read closely enough; the application note says "f_s/8 (or 6000Hz)", because for the purpose of where that's written, the sampling rate is 48000Hz.
However, that is a very narrowing look at filters; drawing the angles involved in equations 4,5,6 from that applications notes into an s-plane diagram this looks like it'd make sense, but these aren't the only filter options there are. The point the AN makes is that these are simple formulas, that approximate a "good" filter (because designing an IIR is usually a bit more complex), and they can only be used below f_2/8. I haven't tried to figure out what happens mathematically at higher frequencies, but I just guess the filters aren't as nicely uniform afterwards.
So, my approach would simply be using any filter design tool to calculate coefficients for you. You could, for example, use Matlab's filter design tool, or you could use GNU Radio's gr_filter_design, to give you IIRs. However, automatically found IIRs will usually be longer than 3 taps, unless you know very well how you mathematically define your design requirements so that the algorithm does what you want.
As much as I like the approach of using IIRs for audio equalization, where phase doesn't matter, I'd say the approach in the Application node is not easy to understand unless one has very solid background in filter/system theory. I guess you'd either study some signal theory with an electrical engineering textbook, or you just accept the coefficients as they are given on p. 28ff.
I've written a GA to model a handful of stocks (4) over a period of time (5 years). It's impressive how quickly the GA can find an optimal solution to the training data, but I am also aware that this is mainly due to it's tendency to over-fit in the training phase.
However, I still thought I could take a few precautions and and get some kind of prediction on a set of unseen test stocks from the same period.
One precaution I took was:
When multiple stocks can be bought on the same day the GA only buys one from the list and it chooses this one randomly. I thought this randomness might help to avoid over-fitting?
Even if over-fitting is still occurring,shouldn't it be absent in the initial generations of the GA since it hasn't had a chance to over-fit yet?
As a note, I am aware of the no-free-lunch theorem which demonstrates ( I believe) that there is no perfect set of parameters which will produce an optimal output for two different datasets. If we take this further, does this no-free-lunch theorem also prohibit generalization?
The graph below illustrates this.
->The blue line is the GA output.
->The red line is the training data (slightly different because of the aforementioned randomness)
-> The yellow line is the stubborn test data which shows no generalization. In fact this is the most flattering graph I could produce..
The y-axis is profit, the x axis is the trading strategies sorted from worst to best ( left to right) according to there respective profits (on the y axis)
Some of the best advice I've received so far (thanks seaotternerd) is to focus on the earlier generations and increase the number of training examples. The graph below has 12 training stocks rather than just 4, and shows only the first 200 generations (instead of 1,000). Again, it's the most flattering chart I could produce, this time with medium selection pressure. It certainly looks a little bit better, but not fantastic either. The red line is the test data.
The problem with over-fitting is that, within a single data-set it's pretty challenging to tell over-fitting apart from actually getting better in the general case. In many ways, this is more of an art than a science, but here are some general guidelines:
A GA will learn to do exactly what you attach fitness to. If you tell it to get really good at predicting one series of stocks, it will do that. If you keep swapping in different stocks to predict, though, you might be more successful at getting it to generalize. There are a few ways to do this. The one that has had perhaps the most promising results for reducing over-fitting is imposing spatial structure on the population and evaluating on different test cases in different cells, as in the SCALP algorithm. You could also switch out the test cases on a time basis, but I've had more mixed results with that sort of an approach.
You are correct that over-fitting should be less of a problem early on. Generally, the longer you run a GA, the more over-fitting will be possible. Typically, people tend to assume that the general rules will be learned first, before the rote memorization of over-fitting takes place. However, I don't think I've actually ever seen this studied rigorously - I could imagine a scenario where over-fitting was so much easier than finding general rules that it happens first. I have no idea how common that is, though. Stopping early will also reduce the ability of the GA to find better general solutions.
Using a larger data-set (four stocks isn't that many) will make your GA less susceptible to over-fitting.
Randomness is an interesting idea. It will definitely hurt the GA's ability to find general rules, but it should also reduce over-fitting. Without knowing more about the specifics of your algorithm, it's hard to say which would win out.
That's a really interesting thought about the no free lunch theorem. I'm not 100% sure, but I think it does apply here to some extent - better fitting some data will make your results fit other data worse, by necessity. However, as wide as the range of possible stock behaviors is, it is much narrower than the range of all possible time series in general. This is why it is possible to have optimization algorithms at all - a given problem that we are working with tends produce data that cluster relatively closely together, relative to the entire space of possible data. So, within that set of inputs that we actually care about, it is possible to get better. There is generally an upper limit of some sort on how well you can do, and it is possible that you have hit that upper limit for your data-set. But generalization is possible to some extent, so I wouldn't give up just yet.
Bottom line: I think that varying the test cases shows the most promise (although I'm biased, because that's one of my primary areas of research), but it is also the most challenging solution, implementation-wise. So as a simpler fix you can try stopping evolution sooner or increasing your data-set.
I am learning directx. It provides a huge amount of freedom in how to do things, but presumably different stategies perform differently and it provides little guidance as to what well performing usage patterns might be.
When using directx is it typical to have to swap in a bunch of new data multiple times on each render?
The most obvious, and probably really inefficient, way to use it would be like this.
Stragety 1
On every single render
Load everything for model 0 (textures included) and render it (IASetVertexBuffers, VSSetShader, PSSetShader, PSSetShaderResources, PSSetConstantBuffers, VSSetConstantBuffers, Draw)
Load everything for model 1 (textures included) and render it (IASetVertexBuffers, VSSetShader, PSSetShader, PSSetShaderResources, PSSetConstantBuffers, VSSetConstantBuffers, Draw)
etc...
I am guessing you can make this more efficient partly if the biggest things to load are given dedicated slots, e.g. if the texture for model 0 is really complicated, don't reload it on each step, just load it into slot 1 and leave it there. Of course since I'm not sure how many registers there are certain to be of each type in DX11 this is complicated (can anyone point to docuemntation on that?)
Stragety 2
Choose some texture slots for loading and others for perpetual storage of your most complex textures.
Once only
Load most complicated models, shaders and textures into slots dedicated for perpetual storage
On every single render
Load everything not already present for model 0 using slots you set aside for loading and render it (IASetVertexBuffers, VSSetShader, PSSetShader, PSSetShaderResources, PSSetConstantBuffers, VSSetConstantBuffers, Draw)
Load everything not already present for model 1 using slots you set aside for loading and render it (IASetVertexBuffers, VSSetShader, PSSetShader, PSSetShaderResources, PSSetConstantBuffers, VSSetConstantBuffers, Draw)
etc...
Strategy 3
I have no idea, but the above are probably all wrong because I am really new at this.
What are the standard strategies for efficient rendering on directx (specifically DX11) to make it as efficient as possible?
DirectX manages the resources for you and tries to keep them in video memory as long as it can to optimize performance, but can only do so up to the limit of video memory in the card. There is also overhead in every state change even if the resource is still in video memory.
A general strategy for optimizing this is to minimize the number of state changes during the rendering pass. Commonly this means drawing all polygons that use the same texture in a batch, and all objects using the same vertex buffers in a batch. So generally you would try to draw as many primitives as you can before changing the state to draw more primitives
This often will make the rendering code a little more complicated and harder to maintain, so you will want to do some profiling to determine how much optimization you are willing to do.
Generally you will get better performance increases through more general algorithm changes beyond the scope of this question. Some examples would be reducing polygon counts for distant objects and occlusion queries. A popular true phrase is "the fastest polygons are the ones you don't draw". Here are a couple of quick links:
http://msdn.microsoft.com/en-us/library/bb147263%28v=vs.85%29.aspx
http://www.gamasutra.com/view/feature/3243/optimizing_direct3d_applications_.php
http://http.developer.nvidia.com/GPUGems2/gpugems2_chapter06.html
Other answers are better answers to the question per se, but by far the most relevant thing I found since asking was this discussion on gamedev.net in which some big title games are profiled for state changes and draw calls.
What comes out of it is that big name games don't appear to actually worry too much about this, i.e. it can take significant time to write code that addresses this sort of issue and the time it takes to spend writing code fussing with it probably isn't worth the time lost getting your application finished.
For instance:
An approach to compute efficiently the first intersection between a viewing ray and a set of three objects: one sphere, one cone and one cylinder (other 3D primitives).
What you're looking for is a spatial partitioning scheme. There are a lot of options for dealing with this, and lots of research spent in this area as well. A good read would be Christer Ericsson's Real-Time Collision Detection.
One easy approach covered in that book would be to define a grid, assign all objects to all cells it intersects, and walk along the grid cells intersecting the line, front to back, intersecting with each object associated with that grid cell. Keep in mind that an object might be associated with more grid-cells, so the intersection point computed might actually not be in the current cell, but actually later on.
The next question would be how you define that grid. Unfortunately, there's no one good answer, and you need to consider what approach might fit your scenario best.
Other partitioning schemes of interest are different tree structures, such as kd-, Oct- and BSP-trees. You could even consider using trees combined with a grid.
EDIT
As pointed out, if your set is actually these three objects, you're definately better of just intersecting each one, and just pick the earliest one. If you're looking for ray-sphere, ray-cylinder, etc, intersection tests, these are not really hard and a quick google should supply all the math you might possibly need. :)
"computationally efficient" depends on how large the set is.
For a trivial set of three, just test each of them in turn, it's really not worth trying to optimise.
For larger sets, look at data structures which divide space (e.g. KD-Trees). Whole chapters (and indeed whole books) are dedicated to this problem. My favourite reference book is An Introduction to Ray Tracing (ed. Andrew. S. Glassner)
Alternatively, if I've misread your question and you're actually asking for algorithms for ray-object intersections for specific types of object, see the same book!
Well, it depends on what you're really trying to do. If you'd like to produce a solution that is correct for almost every pixel in a simple scene, an extremely quick method is to pre-calculate "what's in front" for each pixel by pre-rendering all of the objects with a unique identifying color into a background item buffer using scan conversion (aka the z-buffer). This is sometimes referred to as an item buffer.
Using that pre-computation, you then know what will be visible for almost all rays that you'll be shooting into the scene. As a result, your ray-environment intersection problem is greatly simplified: each ray hits one specific object.
When I was doing this many years ago, I was producing real-time raytraced images of admittedly simple scenes. I haven't revisited that code in quite a while but I suspect that with modern compilers and graphics hardware, performance would be orders of magnitude better than I was seeing then.
PS: I first read about the item buffer idea when I was doing my literature search in the early 90s. I originally found it mentioned in (I believe) an ACM paper from the late 70s. Sadly, I don't have the source reference available but, in short, it's a very old idea and one that works really well on scan conversion hardware.
I assume you have a ray d = (dx,dy,dz), starting at o = (ox,oy,oz) and you are finding the parameter t such that the point of intersection p = o+d*t. (Like this page, which describes ray-plane intersection using P2-P1 for d, P1 for o and u for t)
The first question I would ask is "Do these objects intersect"?
If not then you can cheat a little and check for ray collisions in order. Since you have three objects that may or may not move per frame it pays to pre-calculate their distance from the camera (e.g. from their centre points). Test against each object in turn, by distance from the camera, from smallest to largest. Although the empty space is the most expensive part of the render now, this is more effective than just testing against all three and taking a minimum value. If your image is high res then this is especially efficient since you amortise the cost across the number of pixels.
Otherwise, test against all three and take a minimum value...
In other situations you may want to make a hybrid of the two methods. If you can test two of the objects in order then do so (e.g. a sphere and a cube moving down a cylindrical tunnel), but test the third and take a minimum value to find the final object.