Estimating layout of cluttered indoor scenes using trajectory-based priors

Shoaib, Muhammad; Yang, Michael Ying; Rosenhahn, Bodo; Ostermann, Jöern

doi:10.1016/j.imavis.2014.07.003

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…As many environments, particularly indoor scenes, have been designed for people's daily usage, human behavioral priors can be leveraged to additionally reason about 2D or 3D scene observations. Various methods have been proposed to leverage human context as extra signal towards holistic perception to improve performance in scene understanding tasks such as semantic segmentation [11], layout detection from images [17,60], 3D object labeling [30], 3D object detection and segmentation [66], and 3D reconstruction [18,19].…”

Section: Related Workmentioning

confidence: 99%

Pose2Room: Understanding 3D Scenes from Human Activities

Nie¹,

Dai²,

Han³

et al. 2021

Preprint

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2 SRIBD, CUHKSZ Figure 1. From an observed pose trajectory of a person performing daily activities in an indoor scene (left), we learn to estimate likely object configurations of the scene underlying these interactions, as set of object class labels and oriented 3D bounding boxes (middle). By sampling from our probabilistic decoder, we synthesize multiple plausible object arrangements (right). (Scene geometry is shown only for visualization.)

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Section: Related Workmentioning

confidence: 99%

Pose2Room: Understanding 3D Scenes from Human Activities

Nie¹,

Dai²,

Han³

et al. 2021

Preprint

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“…The background region of an image can be extracted by using line segmentation as introduced in Ramalingam et al is method [27]. For complex background, the approach of Shoaib et al [31] which used key points to estimate the wall, ceiling and floor can be adopted. In addition, the concept of texture classification for detecting wall, ceiling and floor proposed by Hödlmoser and Micusik [32] may also be used.…”

Section: Assumptionsmentioning

confidence: 99%

Automatic video similarity measure using vectorized attributed graph matching

Veeraprasit

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The video scene similarity is introduced in term of the objects' positions analysis. The background objects' positions which are extracted from the video scene are transformed into the spatial logical functions. The spatial logical function which is used to represent the objects' positions on the scene are decoded into top-view model which is projected on the grid unit plane later. From the simulated grid unit plane, the video scene top-view model information are kept in digital form which can be used variously and easily. Besides those data in grid unit plane are the features in the semantic spatial graph. The transformation from unit plane into graph is described in this dissertation. The semantic spatial graph is used to identify the pattern of the objects' position in a scene. Due to the characteristic of semantic spatial graph, the comparing between video scenes are simplified. The transformation to top-view model is robust to perspective. Eventhough the view are changed perspectively, the semantic spatial graph can determined the similarity through top-view model. The robustness of this algorithm are showed by the some examples of the experiments.

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