Stephen Lawler: Look! Up in the sky! It's Virtual Earth!

19,210 views ・ 2007-06-21

TED


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譯者: 張新永 Davidchang 審譯者: HUI YU
00:25
What I want to talk to you about today is
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今天我想要跟大家介紹
00:28
virtual worlds, digital globes, the 3-D Web, the Metaverse.
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虛擬世界,也有人稱為數位地球,三維網路或是網路虛擬世界.
00:37
What does this all mean for us?
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虛擬世界到底會對我們有甚麼影響?
00:39
What it means is the Web is going to become an exciting place again.
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它代表的意義是網路將將再度成為讓人心動的空間.
00:44
It's going to become super exciting as we transform
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當我們化身進入這個以圖像,高計算能力和低延遲
00:47
to this highly immersive and interactive world.
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所構成的深入及高互動數位世界
00:51
With graphics, computing power, low latencies,
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一切都會變成超high的
00:54
these types of applications and possibilities
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這類型的應用及潛在發展
00:57
are going to stream rich data into your lives.
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將把豐富的資料源源不斷的帶入人類的生活世界
01:02
So the Virtual Earth initiative, and other types of these initiatives,
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所以,虛擬地球及其他這種類型的倡議
01:07
are all about extending our current search metaphor.
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都和擴展我們目前資料搜尋方法有關.
01:13
When you think about it, we're so constrained by browsing the Web,
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回想一下,我們目前在流覽網路時所受的限制吧.
01:16
remembering URLs, saving favorites.
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我們必須記住網址,保存到"我的最愛".
01:19
As we move to search, we rely on the relevance rankings,
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當開始進行搜索,我們必須依賴相關性排名,
01:22
the Web matching, the index crawling.
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網路匹配和索引檢索等方式找資料.¥
01:25
But we want to use our brain!
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但我們希望用我們的大腦來找資料!
01:27
We want to navigate, explore, discover information.
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我們希望穿梭在網路世界中,自主的探索和發現資訊.
01:30
In order to do that, we have to put you as a user back in the driver's seat.
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為了達成這個目的,我們必須把使用者放在主導搜尋方向的位置上.
01:35
We need cooperation between you and the computing network and the computer.
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我們需要在人與網路和電腦之間建立一個合作空間.
01:39
So what better way to put you back in the driver's seat
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就把使用者放在主導搜尋方向的位置上而言, 有什麼方法會比
01:43
than to put you in the real world that you interact in every day?
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把人放在每天都在互動的真實世界裏還要好呢?♫
01:46
Why not leverage the learnings that you've been learning your entire life?
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為什麼不直接利用你過去生活中學過的知識呢?
01:50
So Virtual Earth is about starting off
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所以,一開始虛擬地球就是要
01:53
creating the first digital representation, comprehensive, of the entire world.
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第一個將整個地球以數位方式,深入且全面性的呈現出來
01:58
What we want to do is mix in all types of data.
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我們將混合所有類型的資料.
02:01
Tag it. Attribute it. Metadata. Get the community to add local depth,
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給它做上標記,加上屬性和元資料. 接觸網路社群並加上本地內容
02:06
global perspective, local knowledge.
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-- 也就是以全球的視野整合在地的知識.
02:09
So when you think about this problem,
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那麼,當你體會到要解決這個問題
02:11
what an enormous undertaking. Where do you begin?
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須要進行許多工作. 你會由哪兒著手做呢?
02:15
Well, we collect data from satellites, from airplanes,
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我們通過衛星,飛機
02:19
from ground vehicles, from people.
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從地面車輛及人群中收集資料
02:22
This process is an engineering problem,
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整個過程涉及工程.
02:27
a mechanical problem, a logistical problem, an operational problem.
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機械,邏輯問題和如何營運等問題.
02:31
Here is an example of our aerial camera.
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用圖片中空照攝影機當作例子.
02:33
This is panchromatic. It's actually four color cones.
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這是一個全色相機, 它具有四個彩色錐體.
02:36
In addition, it's multi-spectral.
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另外, 它是多光譜的.
02:38
We collect four gigabits per second of data,
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我們每秒種可以採集到4GB的資料,
02:42
if you can imagine that kind of data stream coming down.
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如何想像這樣傳輸下來的資料流有多大,
02:44
That's equivalent to a constellation of 12 satellites at highest res capacity.
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那相當於12個衛星組成的星座以最高解析度所拍攝的資料量.
02:50
We fly these airplanes at 5,000 feet in the air.
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我們駕駛這些飛機到5000英尺的高空;
02:54
You can see the camera on the front. We collect multiple viewpoints,
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你可以看到放置在前端的攝影機. 我們在不同的視點,
02:57
vantage points, angles, textures. We bring all that data back in.
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有利位置, 角度, 結構收集資料. 然後把資料帶回來.
03:03
We sit here -- you know, think about the ground vehicles, the human scale --
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所以現在我們可以坐在這兒親眼觀看 地面車輛和人的比例.
03:07
what do you see in person? We need to capture that up close
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你親眼看到了甚麼? 我們需要近距離捕捉影像
03:09
to establish that what it's like-type experience.
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來建立更逼真的體驗.
03:13
I bet many of you have seen the Apple commercials,
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我想你們大多數人都看過Apple商業展廣告吧,
03:17
kind of poking at the PC for their brilliance and simplicity.
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它以嘲笑PC電腦的方式凸顯自己產品的的簡潔和光輝.♪
03:23
So a little unknown secret is --
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那麼, 有一個不為人知秘密是 --
03:25
did you see the one with the guy, he's got the Web cam?
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你有沒有看到一個帶著網路攝影機的傢伙?
03:29
The poor PC guy. They're duct taping his head. They're just wrapping it on him.
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那個可憐的PC傢伙, 他們把管子直接用膠帶黏在他頭上, 他們把他包裝成那樣兒.
03:33
Well, a little unknown secret is his brother actually works on the Virtual Earth team.
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但, 鮮為人知的秘密是,他的哥哥其實在虛擬地球研發團隊工作.
03:37
(Laughter). So they've got a little bit of a sibling rivalry thing going on here.
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(笑聲) 所以那裏呈現一些兄弟之間相互抗衡的情形.
03:42
But let me tell you -- it doesn't affect his day job.
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但我告訴你 -- 這沒有影響他每天的工作.
03:44
We think a lot of good can come from this technology.
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我們認為這個科技可以帶來很多的好處.
03:47
This was after Katrina. We were the first commercial fleet of airplanes
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這是在Katrina颶風之後. 我們當時是進入災區的
03:51
to be cleared into the disaster impact zone.
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第一個商業飛機團隊
03:54
We flew the area. We imaged it. We sent in people. We took pictures of interiors,
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我們飛到那個區域,拍下照片, 傳送給大家.
03:59
disaster areas. We helped with the first responders, the search and rescue.
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我們深入災區拍攝圖片. 我們幫助緊急救生, 搜索和營救等團隊.
04:03
Often the first time anyone saw what happened to their house was on Virtual Earth.
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許多災區的民眾是藉由虛擬地球的影像首次看到他們房屋當時的情況.
04:08
We made it all freely available on the Web, just to --
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我們經由網路把這些影像免費提供給所有人, 因為
04:10
it was obviously our chance of helping out with the cause.
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這顯然是我們可以為災區人們提供幫助的機會.♫
04:14
When we think about how all this comes together,
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當我們思考如何將這些資料組合在一起.
04:17
it's all about software, algorithms and math.
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面對的全是關於電腦軟體, 演算法和數學問題.
04:21
You know, we capture this imagery but to build the 3-D models
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我們已經擷取圖像了,但是為了構建三維模型,
04:24
we need to do geo-positioning. We need to do geo-registering of the images.
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我們還需要做地理定位. 我們需要在圖片上作地理標記.
04:29
We have to bundle adjust them. Find tie points.
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我們必須收集調整他們. 找到聯結點.♪
04:31
Extract geometry from the images.
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在圖像中萃取出幾何關係. .
04:34
This process is a very calculated process.
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整個過程需要許多計算的程序
04:38
In fact, it was always done manual.
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實際上,這些計算在過去是用人工自己去計算的¼
04:39
Hollywood would spend millions of dollars to do a small urban corridor
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好萊塢為了它的電影必須花數百萬美金去將一個小市區走廊圖像數位化
04:43
for a movie because they'd have to do it manually.
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這是因為他們必須用人工逐步計算.
04:46
They'd drive the streets with lasers called LIDAR.
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他們在車輛上裝置雷射掃描探測儀(lLIDAR)到街道掃描.
04:48
They'd collected information with photos. They'd manually build each building.
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他們收集圖片資訊; 他們以人工處理每個大樓的數位化.
04:52
We do this all through software, algorithms and math --
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而我們則將這些工作交給電腦軟體, 演算法和數學處理.
04:54
a highly automated pipeline creating these cities.
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所以我們是用一個自動化的生產管道來建造這些城市.
04:57
We took a decimal point off what it cost to build these cities,
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所以我們建造這些虛擬城市的費用只要十分之一
05:00
and that's how we're going to be able to scale this out and make this reality a dream.
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這就是我們為什麼能把規模做大,並且將現實轉化成虛擬.
05:04
We think about the user interface.
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我們也考慮到使用者介面.
05:06
What does it mean to look at it from multiple perspectives?
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採用多視角意味著什麼?
05:09
An ortho-view, a nadir-view. How do you keep the precision of the fidelity of the imagery
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一個鳥瞰視角, 一個垂直俯視. 你要如何在必須保持圖像的精度的
05:14
while maintaining the fluidity of the model?
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同時 也保持模型的流暢性?
05:18
I'll wrap up by showing you the --
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最後我想展示給各位看的是 --
05:20
this is a brand-new peek I haven't really shown into the lab area of Virtual Earth.
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這是個全新的俯視圖 我還沒有放入虛擬地球的實驗室區域.
05:24
What we're doing is -- people like this a lot,
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我們要做的是 -- 人們喜歡這個
05:27
this bird's eye imagery we work with. It's this high resolution data.
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我們現在要處理的鳥瞰圖是高解析度的資料.
05:30
But what we've found is they like the fluidity of the 3-D model.
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你們可以看到他們在運作時仍能維持三維模型的流暢度.
05:34
A child can navigate with an Xbox controller or a game controller.
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孩子們可以使用Xbox或遊戲機去探索.
05:38
So here what we're trying to do is we bring the picture and project it into the 3-D model space.
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這裏,我們正嘗試將圖片映射到三維模型空間中.
05:43
You can see all types of resolution. From here, I can slowly pan the image over.
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你可以用不同的解析度觀看. 從這裏,我可以慢慢平移圖像.
05:49
I can get the next image. I can blend and transition.
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我可以看到下一張圖片,我可以混合及轉換
05:52
By doing this I don't lose the original detail. In fact, I might be recording history.
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做這些操作時,我不會損失任何細節. 實際上,我有可能在記錄歷史.
05:57
The freshness, the capacity. I can turn this image.
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新奇,能力. 我可以轉動這個圖像.
06:00
I can look at it from multiple viewpoints and angles.
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由多視點和多角度來觀查看這圖.
06:03
What we're trying to do is build a virtual world.
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我們企圖打造一個虛擬世界.
06:06
We hope that we can make computing a user model you're familiar with,
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我們希望用一個一般人熟悉的使用者模型.
06:11
and really derive insights from you, from all different directions.
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就可以從你的角度,從所有不同的方向流覽.
06:15
I thank you very much for your time.
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謝謝大家花這麼多時間聽講.
06:17
(Applause)
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