What's Going On
lucida commented on slide_023 of Numerical Integration ()

The derivative of the CDF P(x) is the pdf p(x).

kmcrane commented on slide_040 of Introduction to Geometry ()

@pavelkang: Nominally, it's O(n^3) in 3D and O(n^2) in 2D. :-)

lucida commented on slide_040 of Introduction to Geometry ()

@pavelkang because you also need to store the value of the function at that x,y location


oops! read that wrong, storing the value of the function still makes it O(n^2) in 2d

BryceSummers commented on slide_061 of Advanced Sampling and Rendering ()

Huh, so perfect hashing is kind of like an asymptotic certification of hashing algorithms, in that they have theoretical probabilistic guarantees, but we can also construct a system that is practically guaranteed to match the efficiency expectations.

This also seems kind of like the halting problem in that we must ask ourselves given more time will the value of this pixel change or will it stay the same no matter how long we run our monte carlo estimator. Are we "done".

kmcrane commented on slide_001 of Rendering Challenges of VR ()

Amazing that you humans can't see further into infrared.

kmcrane commented on slide_041 of Fluid Simulation ()

@ak-47 Correct---that's the difference between using reduced coordinates and using constraints, as outlined a few slides later.

kmcrane commented on slide_037 of Fluid Simulation ()

Another way of thinking about it is: assume you're starting out with a homogeneous fluid that has not been compressed. At this initial moment in time, (\rho) is certainly constant. Now what has to be true about the velocity field that takes us forward to the "next moment in time" in order to remain incompressible? This condition is expressed in terms of the current mass density, which is constant. Again, no circular reasoning there.

kmcrane commented on slide_037 of Fluid Simulation ()

Assuming conservation of mass, we get the first equation. Assuming we're working with a liquid that has constant mass density (e.g., water rather than oil & vinegar) we get the second equation. We didn't start with any assumption on the velocity field (u), so the argument isn't circular---we're just re-expressing our assumption about incompressibility in terms of the velocity.

kmcrane commented on slide_024 of Fluid Simulation ()

@ak-47 If you replace "day" with "yoctosecond," then yes. This is the instantaneous change in the temperature due both to the fact that the fluid may be heating up/cooling down, and the fact that it may be moving.

ak-47 commented on slide_014 of Rendering Challenges of VR ()

Understanding check: Images at different virtual depth in VR require different vergences (because parallax is preserved in VR), but the accommodation stays constant (because the screen is still the same difference from your eye)?

ak-47 commented on slide_001 of Rendering Challenges of VR ()

Amazing that Skynet would settle for monochromatic vision on the Terminator.

ak-47 commented on slide_041 of Fluid Simulation ()

It seems like we have two options to solve this: add a LaGrangian constraint OR just use $\theta$ as our variable and treat the length as a constant when doing our calculus.

ak-47 commented on slide_037 of Fluid Simulation ()

"Since density is constant, we get our incompressibility constraint"...that sounds like circular logic? What is the empirical fact we start with here and what do we conclude from it?

ak-47 commented on slide_024 of Fluid Simulation ()

Understanding check: This is the difference you'd get between consecutive readings by sticking your thermometer in a single spot of a moving fluid.

E.g. you put a thermometer at The Point, let the rivers flow for a day, then put the thermometer at The Point again, and the difference is the material derivative.

kmcrane commented on slide_061 of Advanced Sampling and Rendering ()

@BryceSummers: Those phenomena are certainly needed to get a "correct" image in the sense that it matches real-world experiments, but the idea on this slide is a bit different. Here we're saying: even if you allow a relatively simple model of scattering, etc., can you guarantee that the program produces a correct image within some given tolerance (\epsilon)? In other words, can you come up with a sampling strategy that guarantees the numerical estimate of an integral agrees with the true integral (up to a fixed tolerance)? This problem actually doesn't have anything to do with rendering: consider numerically integrating a function (f: \mathbb{R} \to \mathbb{R}). If this function looks like a tiny spike (not quite a Dirac delta, but almost), then we could throw billions of samples at the problem without ever getting a nonzero value. One might then (incorrectly) conclude that the integral is zero, even though the magnitude of the spike (and hence the integral) can be arbitrarily large.

kmcrane commented on slide_060 of Advanced Sampling and Rendering ()

@dvernet: The most important thing to keep in mind is that there is always some reasonable chance that you'll transition to a darker sample; we're not always going from dark to light. This basically has to be true in order for anything like Metropolis-Hastings to make sense: if we just accepted all transitions with, say, probability 1/2 (independent of the sample value), then our final collection of samples would look nothing like the integrand. That's why we have to go "uphill more" and "downhill less." (Of course, one must always prove that all of this converges... but that's the intuition.)

kmcrane commented on slide_024 of Advanced Sampling and Rendering ()

@dvernet: Because in that example I said that the function I'm integrating is half red and half blue. ;-) In other words, the importance density isn't picking the sample location based on color; it's picking it based on the location in the domain. So if half of the domain is red, and half of the domain is blue, but we have an importance sampling strategy that yields a very different distribution of samples, then we are doing something wrong!

kmcrane commented on slide_016 of Advanced Sampling and Rendering ()

Completely understanding sources of bias in Monte Carlo rendering can be a rather tricky issue. For instance, suppose you use interpolated vertex normals for shading---already your result will be biased, because the illumination does not correspond to the actual geometry. So, you have to be careful. If you want to understand all the issues in great detail, a great place to look is the thesis by Veach.

kmcrane commented on slide_030 of Frequency, Vibration, and Fourier ()

@whdawn: Not sure I understand your question, but yes, in an intuitive sense the L2 inner product captures something about "how much two functions overlap," just like the Euclidean inner product captures something about "how much two vectors line up."

kmcrane commented on slide_013 of Frequency, Vibration, and Fourier ()

@whdawn: The short answer is that they use frequency information to choose their samples more intelligently; the long answer is that you should read their paper and find out! :-)

kmcrane commented on slide_051 of Visual and Numerical Linear Algebra ()

@kapalani: Yep. And the even more amazing fact is that Eigen is not particularly fast, as far as linear solvers go! For sparse linear algebra, a lot of "heavy duty" numerical linear algebra is done using a package called SuiteSparse (for instance, this is what gets used inside MATLAB and Mathematica). In some sense, it shouldn't be too surprising that sparse linear solvers scale well, since (and this is only an extremely rough piece of intuition) the number of nonzeros is roughly linear rather than quadratic in the matrix dimension. So at least something like matrix-vector multiplication shouldn't be expensive. On the other hand, numerical linear algebra is one of the great successes of 20th century scientific computing... as indicated by this slide! In short: you should be impressed! :-)

kmcrane commented on slide_036 of Visual and Numerical Linear Algebra ()

Well, my matrices didn't format correctly... but hopefully you get the idea. :-)

kmcrane commented on slide_036 of Visual and Numerical Linear Algebra ()

@pavelkang: I think those are all good thoughts. Another way to do it is to say: in n dimensions, minus the identity matrix $-I$ can be expressed as the product of n elementary reflections about each of the axes. E.g.,

$$ \left[ \begin{array}{rrr} -1 & 0 & 0 \ 0 & -1 & 0 \ 0 & 0 & -1 \end{array} \right] = \left[ \begin{array}{rrr} -1 & 0 & 0 \ 0 & 1 & 0 \ 0 & 0 & 1 \end{array} \right]\left[ \begin{array}{rrr} 1 & 0 & 0 \ 0 & -1 & 0 \ 0 & 0 & 1 \end{array} \right]\left[ \begin{array}{rrr} 1 & 0 & 0 \ 0 & 1 & 0 \ 0 & 0 & -1 \end{array} \right]. $$

Since each reflection reverses the orientation (and reversing the orientation twice preserves it), (-I) is orientation-reversing in an odd number of dimensions and orientation-preserving in an even number of dimensions.

kmcrane commented on slide_020 of Visual and Numerical Linear Algebra ()

@WALL-E: Correct. For instance, there is not even an additive identity! (I.e., no number "0" such that 0+x=x+0=x.)

kmcrane commented on slide_011 of Visual and Numerical Linear Algebra ()

@BryceSummers: That's the right idea---the simplex method is a method specifically for solving linear programs (LPs), i.e., minimize a linear objective subject to linear inequality constraints. Actually, the simplex method predates descent-based methods for LPs by quite some time. The main problem, though, was that the simplex method has (worst-case) exponential complexity. Later on, people discovered that you can solve LPs in polynomial time using interior point methods, which are (very roughly speaking) descent-based approaches that account for constraints. Still, the simplex method remains a common method of choice due to (often) good real-world performance on certain problems.

kmcrane commented on slide_034 of Physically-Based Animation and PDEs ()

Since we're not solving a PDE here, it's pretty easy to prescribe arbitrary values and derivatives at the boundary points and find an interpolating function---in fact, that's exactly what we did with cubic spline interpolation in Assignment 4. However, once we actually need to satisfy some additional criteria on the interior, we need to be more careful (as the next few slides reveal).

kmcrane commented on slide_013 of Physically-Based Animation and PDEs ()

@dsaksena: Ok! Let me know if you want to chat more.

kmcrane commented on slide_006 of Physically-Based Animation and PDEs ()

Indeed. Here is the video I showed in class.

kmcrane commented on slide_031 of Introduction to Optimization ()

@kapalani: I suspect that most "real-world" IK problems don't have analytical solutions due to the complexity of real systems, but you're right that having closed-form solutions for simple systems (or even simple components of larger systems) can be useful. One way to see that general IK problems are difficult (and perhaps even impossible) to solve exactly is that many of them boil down to hard problems in algebraic geometry. In general, there are a lot of connections between algebraic geometry and kinematics, path planning, etc., and it is definitely a subject worth studying.

kmcrane commented on slide_028 of Introduction to Optimization ()

Right. By the way, inverse kinematics doesn't always have to be about putting an end effector at a particular position---one could also describe more general (and interesting!) goals, like "the center of mass of the robot/figure/creature should be at a certain location," etc. And, since you've already been through the exercise of starting with an IK goal defined in terms of an energy (in Assignment 4) and going all the way through to a numerical algorithm, you should now be able to design other interesting/fun/useful IK tools. :-)

kmcrane commented on slide_002 of Dynamics and Time Integration ()

@BryceSummers: Right, it's kinematic because one can use, say, a spline curve to approximate any measured data points without having any notion of where this data came from or what it means.

kmcrane commented on slide_033 of Introduction to Animation ()

Good question. In Schoenberg's case (if I remember correctly), he approximates a curve by its height over a given tangent line, because he only wants to consider small displacements. What's the difference between its second derivative of a height function and the curvature of its graph? Quite a bit, actually; take a look at the Wikipedia article on curvature for some explicit calculations.

kmcrane commented on slide_024 of Introduction to Animation ()

Depends on the budget. Take a look at some Saturday morning cartoons; nowhere near as smooth as feature film. Plus, from now on, you'll never be able to not notice how jerky the motion is. :-)

kmcrane commented on slide_034 of Introduction to Animation ()

Right---one typically sticks with low-order polynomials in order to avoid oscillations, but there are polynomials specifically designed to avoid Runge's phenomenon, like the Chebyshev polynomials.

kmcrane commented on slide_026 of Theory of Color ()

@whdawn: You have to integrate them all against the S, M, and L curves, and from there determine what linear combination of the (S, M, and L-coefficients of the) first three give you the coefficients of the fourth laser.

kmcrane commented on slide_024 of Theory of Color ()

@whdawn: I would try not to draw close analogies between the brain and a computer: this is a classic mis-comparison of technologies that has been repeated throughout the ages. For instance, in the 19th century people made all sorts of analogies between the brain and a steam engine! Probably 100 years from now it will be something else entirely. The brain is not a steam engine, nor a computer; it is its own fascinating and complicated mechanism---that deserves a little reverence! :-)

whdawn commented on slide_037 of Introduction to Animation ()

Oh, I see. Thanks Keenan!

whdawn commented on slide_034 of Introduction to Animation ()

Yes, it seems that few people use higher than cubic

Thinking out loud: Note that if you're goal is to capture a lot of photons, making your lens smaller is a bad idea. We see this in ray space; the green band's area is how many rays we capture.

So in bottom little square we fix s and t, and we are making the query "How does the camera at v and u see me?" "Me" is the s t pixel.

I think of it as all the information generated by a 2d array of eyes each producing a 2d image.

So a modern camera's software automatically tries to compensate for bad rows?

Why does the micro lens prefilter the signal?

whdawn commented on slide_024 of Rendering Challenges of VR ()

To solve this distortion, why not display the image on the inner surface of a sphere so the distance from the user to each pixel of the image will be the same.

whdawn commented on slide_017 of Rendering Challenges of VR ()

What do this IR camera do?

I am confused about the images in slides 54 - 59. Are they coming from each part of sensors, and adding up to the whole image, in that correct?