day 3 – SGI 2021

That was the tongue-in-cheek remark our TA Peter Rock made at the end of SGI’s last tutorial week session. For me, a fresh CS graduate who always managed to steer clear of the dark abyss where “real math” hides, that remark hit close to home. I think it is fair to say that even for the most math whizzy of us fellows, whether it felt like drinking from a fire hose or a squeeze bottle, the tutorial week left us undeniably very well hydrated.

The week started with Oded Stein’s lecture, an extremely friendly introduction to this new world of geometry processing. For me, it felt like a mixed bag of known concepts, brand new stuff, and some in-between. Maybe because of this, Oded’s lecture was just the introduction I needed to feel like I could do this thing. Surely enough, a few minutes after Oded finished his lecture, pictures of mangled triangle meshes and Gouraud-shaded goats started to flood our Slack. Not only could we do this, we were doing it!

Finally, Prof. Justin Solomon made some remarks at the end of the day that resonated with me so much; I hope I never let myself forget. I do not remember his exact words, but the message was: “math is hard for everyone. There’s no shame in it. Your job is not to understand things right away. Your job is to ask for help until you do.”

What followed the next day was a lecture by Derek Liu, showing how a few lines of math wizardry in MATLAB could perform shape deformations. Things were starting to look like the SIGGRAPH videos I’ve been drooling over for years! As is to be expected, with great wizardry comes great amounts of students asking for help. I was among them. Yet, Derek was so incredibly patient in walking all of us through each mathematical question and their respective MATLAB lines, I left the session actually knowing how to do those things. For someone who loved the videos but never understood a line of the papers, it was a day to remember.

The day continued, then, with Eris Zhang’s lecture, which took Derek’s concepts even further. It was mind-blowing to think a few months ago, a video of Eris’ work was one of those I was drooling over, and now she was there, teaching us the ropes, using excerpts from the video as an example! To me, that is the perfect picture of what an invaluable experience SGI is.

The third day was a very different experience (for me). Silvia Sellán’s lecture brought me a new point of view for things like splines and NURBS, concepts I had previously taught as a TA for semesters on end (albeit years ago). Her approach was very lighthearted and humorous, yet very grounded, making sure everything was accessible and carefully explained. The exercises were equally well thought out. Even so, I eventually needed to ask for help on a more “mathy” task, but Silvia did not give me the answer. Instead, she smoothly guided me through figuring it out for myself, which was amazing! Her closing remarks were: “now make sure you help others who struggle as well.” To me, this way of thinking summarizes the incredible spirit we’ve been having throughout the program.

The day closed with a talk by Shadertoy’s Íñigo Quilez, which first introduced us to rendering using SDFs, then completely opened Pandora’s box. Íñigo’s message was: “you are future researchers, well, here are some things for you to think about.” As honored as I feel by his trust in us, I must say I was too mesmerized by the rendering possibilities to think about how to solve the issues. As someone who used traditional rendering approaches for a long time, the world of SDFs felt like a trip to wonderland for a Mad Hatter’s tea party. A trip I wish to repeat soon! Again the feeling I had on the previous days is repeated: I had been visiting Shadertoy since before college, looking at the walls of code and wondering how they yielded such mesmerizing results. Well, now Íñigo has shown us the way, and the ball is on our court. Amazing.

I could go on for pages talking about the following days, including the incredible first week of research, but I believe this is better suited for a subsequent post. Overall, I can say that I feel extremely fortunate to be a part of this thing, be hand-fed this select knowledge from such experts in the field, and now have such an overview of what paths I can take in my career. SGI is proving to be much more of a game-changer in my life than I could have ever previously imagined. To everyone who made this possible, my sincerest thank you!!

Silvia Sellán’s talks on Day 3 of SGI gave a large-scale overview of shape representation, which I really appreciated as a Geometry Processing newbie.

First, what is Shape Representation? To describe Geometry Processing, Silvia quoted Alec Jacobson: “Geometry Processing is a subfield of biology.” Just as biological organisms have a life cycle of birth–growth–death, so do shapes: they are born (created in a computer program), then they are manipulated and changed through various algorithms, and then they die–or rather, they leave the computer and go into the world as a finished product, such as a rendered image in a video game. Shape Representation encompasses the methods by which a shape can be created.

Silvia gave us an introduction to Shape Representation by focusing first on 2D methods. Using the example of a simple curve, we looked at four ways of representing the curve in 2D. Each method has its advantages and disadvantages:

The simplest way of representing a curve is a point cloud, or a finite subset of points on the curve. The advantage is that this information is easy to store, since it’s just a list of coordinates. However, it lacks information about how those points are connected, and one point cloud could represent many different shapes.

The second method is a polyline or polygonal line, which consists of a point cloud plus a piecewise linear interpolation between connected points. Like a point cloud, a polyline is easy to store. It’s also easy to determine intersections between them, and easy to query–to determine whether any given point lies on the polyline. However, since a polyline is not differentiable at the vertices, it does not allow for differential quantities such as tangents, normal vectors, or curvature. It also needs many points to be stored in order to visually approximate the desired curve.

Third, we can use a spline, which is a piecewise polynomial interpolation with forced continuous derivatives. Splines have differential continuity and don’t need many points in order to approximate a curve but are more difficult to query and to solve intersections.

Finally, we have implicit shape representations, which use implicit functions to define a shape as the region of the plane where the function equals zero. The advantages of implicit representations include easy computation of boolean operations, and it is also possible to treat the shape as an image and use image processing tools and machine learning algorithms. However, not every shape can be represented implicitly, and they are hard to manipulate for many graphics applications.

These methods of shape representation in 2D formed the scaffolding for us to later build an understanding of shape representation in 3D.