Geometric reasoning for single image structure recovery

Lee, David C; Hebert, Martial; Kanade, Takeo

doi:10.1109/cvpr.2009.5206872

Cited by 362 publications

(302 citation statements)

References 16 publications

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“…We extend this work by using other metrics like maximum entropy and maximum symmetry to estimate the center of the corridor when ceiling lights are not visible. Li and Birchfield [8] deveoped a method for extracting wall-floor boundaries for indoor environments that is much more computationally efficient than previous approaches to perform geometric reasoning on images [4,6]. We extend this work by detecting wall-floor boundaries of typical indoor office environments using low-resolution images.…”

Section: Figmentioning

confidence: 97%

Extracting Minimalistic Corridor Geometry from Low-Resolution Images

Murali

Birchfield

2012

Intelligent Robotics and Applications

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Abstract. We propose a minimalistic corridor representation consisting of the orientation line (center) and the wall-floor boundaries (lateral limit). The representation is extracted from low-resolution images using a novel combination of information theoretic measures and gradient cues. Our study investigates the impact of image resolution upon the accuracy of extracting such a geometry, showing that accurate centerline and wall-floor boundaries can be estimated even in texture-poor environments with images as small as 16 × 12. In a database of 7 unique corridor sequences for orientation measurements, less than 2% additional error was observed as the resolution of the image decreased by 99%. One of the advantages of working at such resolutions is that the algorithm operates at hundreds of frames per second, or equivalently requires only a small percentage of the CPU.

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Section: Figmentioning

confidence: 97%

Extracting Minimalistic Corridor Geometry from Low-Resolution Images

Murali

Birchfield

2012

Intelligent Robotics and Applications

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“…Even though recent papers show an impressive performance in extracting the 3D room frame [26,27] or depth-ordered planes from a single 2D image [28] they are at the moment not precise enough in computing all spatial structures given by, e.g., the furniture, on the desired level of detail. Therefore, existing 2D indoor databases cannot be utilized.…”

Section: The 3d Indoor Databasementioning

confidence: 99%

Indoor Scene Classification Using Combined 3D and Gist Features

Swadzba

Wachsmuth

2011

Computer Vision – ACCV 2010

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Abstract. Scene categorization is an important mechanism for providing high-level context which can guide methods for a more detailed analysis of scenes. State-of-the-art techniques like Torralba's Gist features show a good performance on categorizing outdoor scenes but have problems in categorizing indoor scenes. In contrast to object based approaches, we propose a 3D feature vector capturing general properties of the spatial layout of indoor scenes like shape and size of extracted planar patches and their orientation to each other. This idea is supported by psychological experiments which give evidence for the special role of 3D geometry in categorizing indoor scenes. In order to study the influence of the 3D geometry we introduce in this paper a novel 3D indoor database and a method for defining 3D features on planar surfaces extracted in 3D data. Additionally, we propose a voting technique to fuse 3D features and 2D Gist features and show in our experiments a significant contribution of the 3D features to the indoor scene categorization task.

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“…Under the Manhattan world assumption, every surface belonging to the scene structure is aligned to one of the three orthogonal directions, which can be represented by three vanishing points (VP) on the image. Lee et al [13] uses the detected wall boundaries on the image to estimate VPs and solve the scene structure accordingly. However, those boundaries are not likely to be observed in practice.…”

Section: Related Workmentioning

confidence: 99%

“…Our approach follows the general procedure of [8,10,12,13,15,16] to generate the layout of the room. First, we estimate the three orthogonal vanishing points of the scene to obtain the orientation of the 3D box.…”

Section: Estimating a Room Layoutmentioning

confidence: 99%

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Layout Estimation of Highly Cluttered Indoor Scenes Using Geometric and Semantic Cues

Chao

Choi

Pantofaru

et al. 2013

Image Analysis and Processing – ICIAP 2013

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Abstract. Recovering the spatial layout of cluttered indoor scenes is a challenging problem. Current methods generate layout hypotheses from vanishing point estimates produced using 2D image features. This method fails in highly cluttered scenes in which most of the image features come from clutter instead of the room's geometric structure. In this paper, we propose to use human detections as cues to more accurately estimate the vanishing points. Our method is built on top of the fact that people are often the focus of indoor scenes, and that the scene and the people within the scene should have consistent geometric configurations in 3D space. We contribute a new data set of highly cluttered indoor scenes containing people, on which we provide baselines and evaluate our method. This evaluation shows that our approach improves 3D interpretation of scenes.

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Geometric reasoning for single image structure recovery

Cited by 362 publications

References 16 publications

Extracting Minimalistic Corridor Geometry from Low-Resolution Images

Extracting Minimalistic Corridor Geometry from Low-Resolution Images

Indoor Scene Classification Using Combined 3D and Gist Features

Layout Estimation of Highly Cluttered Indoor Scenes Using Geometric and Semantic Cues

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