Indoor scene modeling from a single image using normal inference and edge features

2017 ◽  
Vol 33 (10) ◽  
pp. 1227-1240 ◽  
Author(s):  
Mingming Liu ◽  
Yanwen Guo ◽  
Jun Wang
Keyword(s):  
Scene Modeling ◽  
Single Image ◽  
Indoor Scene ◽  
Edge Features

scholarly journals Shallow2Deep: Indoor scene modeling by single image understanding

2020 ◽  
Vol 103 ◽  
pp. 107271 ◽  
Author(s):  
Yinyu Nie ◽  
Shihui Guo ◽  
Jian Chang ◽  
Xiaoguang Han ◽  
Jiahui Huang ◽  
...  
Keyword(s):  
Image Understanding ◽  
Scene Modeling ◽  
Single Image ◽  
Indoor Scene

scholarly journals Single image-based data-driven indoor scene modeling

2015 ◽  
Vol 53 ◽  
pp. 210-223 ◽  
Author(s):  
Yan Zhang ◽  
Zicheng Liu ◽  
Zheng Miao ◽  
Wentao Wu ◽  
Kai Liu ◽  
...  
Keyword(s):  
Data Driven ◽  
Scene Modeling ◽  
Single Image ◽  
Indoor Scene

scholarly journals INDOOR ROUTE PLANNING UNDER HEXAGON NETWORK CONSIDERING MULTI-CONSTRAINS

2018 ◽  
Vol XLII-4 ◽  
pp. 693-696
Author(s):  
W. Wang ◽  
T. Ai ◽  
C. Gong
Keyword(s):  
Data Model ◽  
Route Planning ◽  
Multiple Constraints ◽  
Scene Modeling ◽  
A Algorithm ◽  
Indoor Scene ◽  
Route Finding ◽  
Spatial Geometry ◽  
Organic Combination ◽  
The Impact

<p><strong>Abstract.</strong> Indoor route planning, affected by many constraints, needs to take consideration of both the spatial geometry, environmental attribute information of the scene and the application preferences. Thus, it requires a data model that can integrate multiple constraints to model the indoor scene; meanwhile, it’s also necessary to take into account the isotropic features of the indoor pathfinding behavior and the superposition analysis of multi-constraint conditions by the route-finding algorithm. Based on this, this paper proposes a multi-factor constrained A* algorithm model based on the hexagonal grid, that is using an isotropic regular hexagon to model an indoor scene, and taking constraint conditions as heuristic factors to guide the route-finding algorithm so as to get the route. Based on this model, distance, recognition and pedestrian density are used as examples to illustrate the impact of constraints on route planning and their organic combination with scene modeling and route-finding algorithms. The experimental results show that this scheme can effectively take into account the constraints such as distance, landmark strength, and pedestrian heat, thus providing a route that is more in line with the application preferences.</p>

scholarly journals Image Super-Resolution Based on Sparse Representation via Direction and Edge Dictionaries

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xuan Zhu ◽  
Xianxian Wang ◽  
Jun Wang ◽  
Peng Jin ◽  
Li Liu ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Super Resolution ◽  
Training Image ◽  
Image Features ◽  
Single Image ◽  
Edge Structure ◽  
Image Patches ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Edge Features

Sparse representation has recently attracted enormous interests in the field of image super-resolution. The sparsity-based methods usually train a pair of global dictionaries. However, only a pair of global dictionaries cannot best sparsely represent different kinds of image patches, as it neglects two most important image features: edge and direction. In this paper, we propose to train two novel pairs of Direction and Edge dictionaries for super-resolution. For single-image super-resolution, the training image patches are, respectively, divided into two clusters by two new templates representing direction and edge features. For each cluster, a pair of Direction and Edge dictionaries is learned. Sparse coding is combined with the Direction and Edge dictionaries to realize super-resolution. The above single-image super-resolution can restore the faithful high-frequency details, and the POCS is convenient for incorporating any kind of constraints or priors. Therefore, we combine the two methods to realize multiframe super-resolution. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed method. Experimental results demonstrate that our method can recover better edge structure and details.

Automatic 3D Indoor Scene Modeling from Single Panorama

2018 ◽  
Author(s):  
Yang Yang ◽  
Shi Jin ◽  
Ruiyang Liu ◽  
Sing Bing Kang ◽  
Jingyi Yu
Keyword(s):  
Scene Modeling ◽  
Indoor Scene

scholarly journals 3D indoor scene modeling from RGB-D data: a survey

2015 ◽  
Vol 1 (4) ◽  
pp. 267-278 ◽  
Author(s):  
Kang Chen ◽  
Yu-Kun Lai ◽  
Shi-Min Hu
Keyword(s):  
Scene Modeling ◽  
Indoor Scene
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