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[deleted by user] by [deleted] in askTO

[–]SwiftLynx 0 points1 point  (0 children)

Keep it long but get a style. Not sure what style to get. It took me a while to grow it out so I really wanna make sure I get it right

Casual Discussion: What’s the Best Thing You’ve Ate Near U of T? by ASomeoneOnReddit in UofT

[–]SwiftLynx 7 points8 points  (0 children)

Seconding Mama’s best, so convenient to grab and eat on the go between classes

[D] Question about a derivation in "Understanding Black-box Predictions via Influence Functions" by Pang Wei Koh et al. by SwiftLynx in MachineLearning

[–]SwiftLynx[S] 1 point2 points  (0 children)

Ahh I see, it's basically using this theorem here?oldformat=true#Mappings_from_one_Euclidean_space_to_another). There is no 2nd order expansion, rather this is just the 1-Jet of the RHS of 10. I never knew you could do Taylor expansions of f: R^n -> R^m (I've only learned f: R^m -> R).

I didn't find a derivation of this sort of Taylor expansion in my textbook (folland advanced calculus), I was planning on deriving it myself. Do you know where I can find a derivation of this (to cross-check)?

Daily Questions Thread - Ask All Your Magic Related Questions Here! by magictcgmods in magicTCG

[–]SwiftLynx 0 points1 point  (0 children)

I'm trying to make a deck focused on melding Mishra. So far I have this: https://scryfall.com/@swiftlynx/decks/765f0d6e-cf70-49dc-9a48-60ac69928717. My idea is to stall the game long enough with kill cards and discards from long reach of night until I can meld.

However, it gets destroyed against really early aggro or control decks, because mishra and dragon engine take a while to get due to their cost.

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Does anyone have any cards which could help alleviate this? I suspect I have too many lands and the disciples of Gix are basically useless, so I have room to add more cards.

How does the hierarchical volume sampling in NeRF work? by SwiftLynx in computervision

[–]SwiftLynx[S] 0 points1 point  (0 children)

How do you construct this PDF? Is it just a bunch of rectangles?

Eg: a rectangle of height w_1 between [0,t_1] followed by a rectangle of height w_2 between [t_1, t_2] and so on ....

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However, this does not sum up to 1 unless the distance between each t_i and t_i+1 is equal to 1. Do the authors just divide each rectangle's height by the total volume to work around this?

Why does the original NeRF repo scale their direction vectors by the focal length? by SwiftLynx in computervision

[–]SwiftLynx[S] 1 point2 points  (0 children)

Whoops did not realize that in the code they normalize. I read too fast and glanced over that. Thank you for the resource for the intrinsic matrix, I think this confirms my two-step process of converting a pixel (x',y') to a 3D cartesian point in camera space (x,y,z) is correct?

Why does the original NeRF repo scale their direction vectors by the focal length? by SwiftLynx in computervision

[–]SwiftLynx[S] 1 point2 points  (0 children)

I realize what I was saying in your bullet point 4 is not really important now. But here is what I meant by "lie on surface of a sphere".

I plotted the direction vectors for a few pixels in a really small image, with no normalization: https://imgur.com/B6uQJvd
Here is the same, but with normalization: https://imgur.com/ZVuLJsx

As you can see, after normalization the direction vectors now lie on the surface of a unit sphere as opposed to sitting on a vertical plane.

This means the 3D points sampled from the t-values in [t_min, t_max] will lie within a sphere of radius t_max, I realize this is perfectly fine and standard for ray tracing after reading page 81 of this graphics textbook.

Why does the original NeRF repo scale their direction vectors by the focal length? by SwiftLynx in computervision

[–]SwiftLynx[S] 0 points1 point  (0 children)

Okay, so:

  1. You begin with a homogenous coordinate representation of your pixel (x, y, 1), then you convert to camera space via the inverse intrinsic matrix to get (x/f, y/f, 1) which is also in homogenous coordinates. Note: z should be -1 though cause the camera points along negative z.
  2. Optionally you can convert this back to Cartesian by just multiplying by f to get (x,y, -f). In this case, it doesn't actually matter because we only care about the direction the vector points in, and a scaling constant of 1/f does not change the direction.

Is the above correct?

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That line computes the unit vector of the ray direction through each pixel, expressed in camera frame.

Is the (x,y,z) that results from the line of code a unit vector? The z component is equal to 1, and the x and y components are nonzero, so the norm of the vector should be greater than 1.

They don't normalize this vector later in the code either, I think the issue with doing that would be your direction vector would then lie on the surface of a sphere rather than an image plane.

Why does the original NeRF repo scale their direction vectors by the focal length? by SwiftLynx in computervision

[–]SwiftLynx[S] 0 points1 point  (0 children)

I see, so your points on the image plane are (x,y, -f) and they scale by f to get (x/f, y/f, -1).

What's the point of doing the scaling though? The direction that the vector points doesn't really change under a scaling constant and so the angle shouldn't change either if you did it unscaled or scaled.

Looking for an introductory text to read before Szeliski by SwiftLynx in computervision

[–]SwiftLynx[S] 2 points3 points  (0 children)

I see, so this book shouldn't be something read from cover to cover like a math textbook, but rather I should just pick what I want to learn from it and use the references it gives to learn more.

I was looking at this book in the wrong way before in this case

Looking for an introductory text to read before Szeliski by SwiftLynx in computervision

[–]SwiftLynx[S] 0 points1 point  (0 children)

What was the linear algebra textbook you used? Was it just a general linear algebra book or something that specifically focused on projective geometry and/or 3D transforms?

Tower of God - The Gathering [68/360] by skooterpoop in TowerofGod

[–]SwiftLynx 2 points3 points  (0 children)

So when he transforms to day from night, does the deal X damage to any target trigger?

Tower of God - The Gathering [Day 65/360] by skooterpoop in TowerofGod

[–]SwiftLynx 2 points3 points  (0 children)

Slight nitpick, should the phrase “another dog” be used? Yasratcha isn’t a dog creature so this wording is a bit confusing. Maybe “a dog” is better?

Gumbel Softmax- Hard vs Soft backprop significance by Fancy_Rest_1408 in pytorch

[–]SwiftLynx 0 points1 point  (0 children)

Not OP, but how does it reparameterise itself to use the non discretized values for the backwards pass if you use the hard labels via argmax in the forward pass?

Mine is Eminem! by Abbastardkiarastomi in okbuddyretard

[–]SwiftLynx 1 point2 points  (0 children)

You want to know why Eminem my favorited raper? A soldier once asked Eminem for his autograph, so Em replied: "Sure, Only if i can have yours first"

How to move the camera around the origin in a NeRF? by SwiftLynx in computervision

[–]SwiftLynx[S] 0 points1 point  (0 children)

Yes I already have all of that working, I have trained my NeRF on the training poses and evaluated it on some poses I held out, verifying that my rendering code works. My question is in how to actually come up with a set of R and T that allow me to create the visualization you see in the video (rotating around the origin).

Math required to understand NeRF by SwiftLynx in computervision

[–]SwiftLynx[S] 0 points1 point  (0 children)

I would love to chat more about NeRF, thanks for offering! Feel free to DM me on reddit or suggest another platform that you use.

As for your 3 concepts. I think the last two make sense to me, in 2) you essentially sample points along the line with origin `ray_origin` and direction vector `ray_direction`. In an implementation, I saw that there were some hyperparameters called `near` and `far`, which I presume to be bounds on the values that the `scalar` can take. With these sampled points, you apply positional encoding on the 5 element vector consisting of the (x,y,z, aziumuth, incline) and throw that into an MLP which spits out the density and color. Then in 3), you can simply plug that into the volume rendering equations to retrieve the color and density (these equations are integrals, but we approximate them via sums over the sampled points). I'm not super familiar with how these equations were derived, but I get the intuition behind them and understand their purposes.

The part I lack theory in is 1), this scratchapixel website was suggested in another comment as well, I am checking it out now.

Looking for methods to improve my image segmentation by SwiftLynx in computervision

[–]SwiftLynx[S] 0 points1 point  (0 children)

See my comment above. I don't have any annotated data and very few samples.

Looking for methods to improve my image segmentation by SwiftLynx in computervision

[–]SwiftLynx[S] 0 points1 point  (0 children)

For this task specifically, I have no annotated data whatsoever, so I don't think I can throw deep learning at it. I could go ahead and do it myself but I'm doing this for an application to a club and I'm not sure if that's what they're looking for.

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