谷歌街景圖之長場景全景視覺化

Long-Scene Panoramic Visualization for Google Street View Images

張庭慈(Ting-Tzu Chang)

指導教授 ： 莊永裕

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2010年)

https://doi.org/10.6342/NTU.2010.00765

摘要

谷歌街景(Google Street View)現在提供了一個街道瀏覽系統給使用者線上使用，可瀏覽街道遍及全世界大部分區域。系統利用全方向的影像(omnidirectional images)建立一個擬真的360度環景泡泡(bubble)為使用者帶來如臨其境的虛擬行走感。然而，由於使用者在泡泡中行為受到限制以及在泡泡間移動為不連續跳動，系統並沒有辦法為較長的街道提供一個好的視覺摘要。

因此，本篇論文提出一個新的系統呈現谷歌街景環景視覺化。系統只需要使用者輸入起點和終點的住址，便會自動擷取谷歌街景的資料、經由SFM(Struture From Motion)找出路段的立體模型、並用不密集的連續全方向圖為資料藉由圖割(Graph-Cut)最小化目標方程式產生出多視點環景圖(Multi-Viewpoint Panorama)。我們將證明我們的結果相當有用，只需讓使用者看一眼，便可簡單快速得到一段長路程視覺摘要。

關鍵字

谷歌街景 ； Structure from Motion(SFM) ； 環景圖 ； 全方向圖 ； 圖割

並列摘要

Nowadays, Google Street View provides user a street navigating system online available in many areas over the world. The system brings photorealism virtual visit sense by constructing immersive $360^{circ}$ panorama or bubble using omnidirectional images. However, it does not provide a good summary vision of the long street due to its limited action and discretely jumping from bubble to bubble.

As a result, we bring a system presenting Google Street View panoramic visualization. Once user input addresses of the starting point and the goal, the system automatically fetching data from Google Street View, recovering 3D models through SFM framework, and producing multi-viewpoint panorama from these sparse omnidirectional consecutive images by minimizing objective function using Graph-Cut. We show that our result is useful for user to easily and rapidly retrieve a visual summary just one glance of long scene.

並列關鍵字

Google Street View ； Structure from Motion(SFM) ； Panorama ； Omnidirectional Images ； Graph-Cut

參考文獻

long scenes with multi-viewpoint panoramas. Proceedings of SIGGRAPH,

[4] H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool. Surf: Speeded up robust features.

Computer Vision and Image Understanding, 110:346–359, 2008.

maps for driving directions. User Interface Science and Technology, 2009.

[6] R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge