東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Data-Driven Geometric Scene Understa...

Satkin, Scott.

Linked to FindBook

Google Book

Amazon

博客來

Data-Driven Geometric Scene Understanding.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data-Driven Geometric Scene Understanding./
Author:	Satkin, Scott.
Description:	108 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
Contained By:	Dissertation Abstracts International75-02B(E).
Subject:	Engineering, Robotics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3575073
ISBN:	9781303526060

Data-Driven Geometric Scene Understanding.
Satkin, Scott.

Data-Driven Geometric Scene Understanding. - 108 p.

Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2013.

In this thesis, we describe a data-driven approach to leverage repositories of 3D models for scene understanding. Our ability to relate what we see in an image to a large collection of 3D models allows us to transfer information from these models, creating a rich understanding of the scene. We develop a framework for auto-calibrating a camera, rendering 3D models from the viewpoint an image was taken, and computing a similarity measure between each 3D model and an input image. We demonstrate this data-driven approach in the context of geometry estimation and show the ability to find the identities, poses and styles of objects in a scene.

ISBN: 9781303526060Subjects--Topical Terms:

1018454
Engineering, Robotics.

Data-Driven Geometric Scene Understanding.
LDR:02677nam a2200313 4500 001 1960553
005 20140623111235.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303526060
035 $a (MiAaPQ)AAI3575073
035 $a AAI3575073
040 $a MiAaPQ $c MiAaPQ
100 1 $a Satkin, Scott. $3 2096238
245 1 0 $a Data-Driven Geometric Scene Understanding.
300 $a 108 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
500 $a Adviser: Martial Hebert.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2013.
520 $a In this thesis, we describe a data-driven approach to leverage repositories of 3D models for scene understanding. Our ability to relate what we see in an image to a large collection of 3D models allows us to transfer information from these models, creating a rich understanding of the scene. We develop a framework for auto-calibrating a camera, rendering 3D models from the viewpoint an image was taken, and computing a similarity measure between each 3D model and an input image. We demonstrate this data-driven approach in the context of geometry estimation and show the ability to find the identities, poses and styles of objects in a scene.
520 $a We begin by presenting a proof-of-concept algorithm for matching 3D models with input images. Next, we present a series of extensions to this baseline approach. Our goals here are three-fold. First, we aim to produce more accurate reconstructions of a scene by determining both the exact style and size of objects as well as precisely localizing their positions. In addition, we aim to increase the robustness of our scene-matching approach by incorporating new features and expanding our search space to include many viewpoint hypotheses. Lastly, we address the computational challenges of our approach by presenting algorithms for more efficiently exploring the space of 3D scene hypotheses, without sacrificing the quality of results.
520 $a We conclude by presenting various applications of our geometric scene understanding approach. We start by demonstrating the effectiveness of our algorithm for traditional applications such as object detection and segmentation. In addition, we present two novel applications incorporating our geometry estimates: affordance estimation and geometry-aware object insertion for photorealistic rendering.
590 $a School code: 0041.
650 4 $a Engineering, Robotics. $3 1018454
650 4 $a Computer Science. $3 626642
650 4 $a Applied Mathematics. $3 1669109
690 $a 0771
690 $a 0984
690 $a 0364
710 2 $a Carnegie Mellon University. $b Robotics. $3 2096237
773 0 $t Dissertation Abstracts International $g 75-02B(E).
790 $a 0041
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3575073