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  • SPEAKER 1: Remco was previously working at Boeing.

  • And he got his degree from Brown University.

  • And he will tell us about how to simplify dance models with

  • the three buildings while still preserving legibility.

  • Thanks, Emil.

  • REMCO CHANG: Thank you guys for coming here.

  • It's a great privilege to be at Google.

  • So I'm here today to talk about kind of a broader

  • research question that I've been working on.

  • And the idea is try to understanding urban

  • environments through the use of a thing called urban

  • legibility.

  • And I'll talk about that a little bit.

  • So this talk is actually going to get broken down

  • into about two parts.

  • The first half is going to be on simplification of the urban

  • models, while maintaining urban legibility.

  • And this is a talk that I gave at SIGGRAPH this summer.

  • So I apologize to people that were there.

  • It will be a repeat, almost verbatim repeat.

  • And the second half will be on discussion and future work,

  • where I'll be talking about some of the things that we've

  • been working on, as well as some of the things that we

  • would like to be working on in the future.

  • And before I start, I'd just like to talk a little bit

  • about what we're doing at the

  • visualization center at Charlotte.

  • And specifically, one thing that we've been really

  • interested in is in this idea of knowledge visualization.

  • And to give you an example of what we mean by knowledge

  • visualization, you can imagine like you have a screen of

  • labels, there's lots of text, and they're kind of

  • overlapping each other.

  • Now, if somehow you can take this screen and extract some

  • sort of higher knowledge, either from the scene or from

  • the user, then it is theoretically possible that

  • you can focus your resources on the things that you want

  • the user to focus on.

  • So in other words, we think of it as, you want to minimize

  • the resources that you're using, but you want to

  • maximize the information that you're giving to the user.

  • And resource can really be anything.

  • It can be CPU time, it could be a number of polygons.

  • And at this particular case, it really is just a number of

  • pixels that you're using on the screen.

  • To give you an idea what we consider as a great knowledge

  • visualization paper, here's something done by Agrawala and

  • Stolte, who are both at Stanford.

  • They're with Pat Hanrahan's group.

  • And this paper is on rendering effective route maps.

  • And here's an example of directions that would be given

  • by MapQuest. And you see that this direction

  • is physically accurate.

  • It shows you exactly how to get from point a to point b.

  • But that's about all it is.

  • You don't really get a lot of information out of this.

  • Whereas, typically speaking, if you ask somebody to give

  • you directions, this will be something that

  • people would draw you.

  • So this hand sketch thing is not at

  • all physically accurate.

  • I mean, it's showing the highway 101 into

  • a very small amount.

  • Where if emphasis more on how you get onto highway 101, or

  • 110, and how to get off.

  • So of course, in their paper that was published at

  • SIGGRAPH, 2001, they were able to mimic what

  • humans typically do.

  • And in this case, really showing off the important

  • information in this task of giving people

  • directions and maps.

  • So we want to take this idea of knowledge visualization and

  • apply it to urban models.

  • So here's a model of a city in China.

  • And the question is, what is the knowledge in this scene?

  • What is it that we want to be able to

  • preserve and to highlight?

  • To answer that, we turn to this idea of urban legibility.

  • And urban legibility is a term that was made famous by Kevin

  • Lynch in his 1960 book called The Image of the City.

  • So what he did for this book was that he went around the

  • city of Boston, and he just asked local residents, and

  • asked them to sketch out--

  • just kind of use a pen and sketch out their immediate

  • surroundings.

  • So what he actually got was a stack of these images that you

  • see on the right here, where people just simply sketched

  • out, you know, this is where I live, this is a big road

  • around me, and so on.

  • And he took this stack of sketched images, and he

  • categorized the important things into five groups.

  • He categorized into paths, which are highways, railroads,

  • roads, canals.

  • Edges, shore lines or boundaries.

  • Districts, industrial, residential district.

  • Nodes, which you can think of as areas where lots of

  • activities converge.

  • So as an example, Time Square in New York City.

  • And then landmarks.

  • And landmarks can really be anything.

  • It can be a tree, it can be a post sign, it

  • can be a big building.

  • It's whatever people use to navigate themselves in an

  • urban environment.

  • So Kevin Lynch defined this idea of urban legibility as

  • "the ease with which a city's parts may be recognized and

  • can be organized into a coherent pattern." So that's

  • kind of a mouthful.

  • But to me, what that really says is, if you can somehow

  • deconstruct a city into these urban legibility elements, we

  • can still be able to organize a city in a coherent pattern.

  • The use of urban legibility in computer science really goes

  • back a little ways.

  • Ruth Dalton, in her 2002 paper, just chronicles the

  • history of what people have done in computer science in

  • the use of urban legibility.

  • And it kind of broke down to two groups.

  • There's one group that tries to justify whether or not the

  • idea of urban legibility actually makes sense.

  • So what they did was, they tried to figure out if these

  • elements are actually important to human navigation.

  • And what they found out, interesting enough, is that

  • paths, edges, and districts are very important to

  • navigation, but landmarks are kind of questionable.

  • There's some groups that think that it's very useful, there's

  • some groups that say that it's totally useless.

  • And the one element that's missing here is

  • the element of nodes.

  • And people have not really been able to successfully

  • quantify what really a node is.

  • So there hasn't been as much research done on trying to

  • figure out if nodes are helpful at all.

  • And the other group of researchers just use urban

  • legibility, and in particular, in graphics and visualization.

  • Most notably, Ingram and Benford has a whole series of

  • papers where they try to use urban legibility in navigating

  • abstract data spaces.

  • So the question is, why did we decide to use urban

  • legibility?

  • And to give you an idea, here we take an original model.

  • These are a bunch of buildings in our Atlanta data set,

  • looked at from a top down view.

  • This is what you would get if you use a traditional

  • simplification method, such as QSlim.

  • And I'm assuming people know what QSlim is.

  • But what you see is that a lot of the buildings get decimated

  • to a point where it doesn't really look

  • like a building anymore.

  • Whereas, our approach is a little bit different.

  • We take an aggregated approach, and this is

  • what you will get.

  • And if we're apply a texture map onto our model, this is

  • what you end up at the end.

  • So, it's actually really interesting that when we take

  • these four models, and we put in the fly-through scene, just

  • kind of a test scenario, and we measure how many pixels are

  • different from the original model.

  • And this is the graph that we get.

  • So you don't have to look at it carefully.

  • But the important thing here that I'm picking out is that,

  • basically, using all these models, they end up with very,

  • very similar differences in terms of pixel errors.

  • And what that says to us is that, even though you look at

  • these four models and you say, well, they look very

  • different to me.

  • But in effect, if you measure it purely quantitative using

  • pixel errors, they actually come out to be very similar.

  • So what that really says to us is, we can't really just use

  • pixel errors as the driving force behind simplification of

  • urban models.

  • We have to use something a little bit different.

  • We have to use a higher level information in here.

  • And to simplify this, let me just state, our goal for this

  • project is to create simplified urban models that

  • retain the image of the city from any

  • view angles and distances.

  • And as an example of what we get, you see the original

  • model on the left.

  • The middle image shows the model having been reduced to

  • 45% of the polygons.

  • And the last one is 18%.

  • And you kind of see a little bit of a dimming effect

  • across, when it goes from original to less polygons.

  • But the important thing here to notice, that when you're

  • doing this, the important features in

  • the city are retained.

  • So for example, the road here is still kept.

  • The city square area is kept.

  • And you pretty much still get the sense that this is the

  • same city that you're looking at, even though there's only

  • 18% of the polygons in the scene.

  • I'm just going to run the application really quickly,

  • and hopefully, nothing goes wrong.

  • OK.

  • So this is using the Chinese city data set.

  • And this is running live.

  • So as you can see, I can just kind of look around.

  • Move to different places.

  • And here--

  • hold on one second.

  • This is where the demo goes wrong.

  • OK.

  • So I'm just going to start zooming out from this view.

  • AUDIENCE: Can you mention how you got that geometry in the

  • [UNINTELLIGIBLE]?

  • Is that made out [UNINTELLIGIBLE PHRASE].

  • REMCO CHANG: Those textures are totally fake.

  • AUDIENCE: [UNINTELLIGIBLE PHRASE].

  • REMCO CHANG: The geometry is actually real.

  • So what we got was, we got the original footprint

  • information, and we got approximate height information

  • in terms of number of stories, or number

  • of flights per building.

  • And we estimated that each story is about three meters.

  • So the geometry, it's kind of the extrusion of footprints.

  • So it's not real in terms of the true 3D models, but the

  • footprints and the positions are

  • actually absolutely correct.

  • AUDIENCE: Do you [UNINTELLIGIBLE] the fact that

  • [UNINTELLIGIBLE] you get repeated texture patterns?

  • REMCO CHANG: There is definitely some.

  • But I'll talk about that in a little bit.

  • Yes, sir?

  • AUDIENCE: What kind of specification

  • [UNINTELLIGIBLE PHRASE]?

  • REMCO CHANG: As it turns out--

  • I'll get into that a little bit later, too--

  • this is--

  • AUDIENCE: [UNINTELLIGIBLE PHRASE]

  • REMCO CHANG: Oh.

  • OK, sorry.

  • So the question was, what kind of hardware I'm

  • running this on.

  • And the honest truth is, I have no idea.

  • But what I do know is that this is kind of the state of

  • the art laptop from Dell.

  • But as it turns out--

  • I'll explain this a little bit-- but the bottleneck's

  • actually not in the graphics card.

  • It's actually in my crappy code where I'm not

  • transferring data fast enough.

  • It's the pipeline that's actually the

  • bottleneck right now.

  • But that's just my fault.

  • I wrote some crappy code.

  • So here I'm just zooming out from

  • that particular viewpoint.