HMD-Guided Image-Based Modeling and Rendering of Indoor Scenes

2018 ◽  
pp. 73-93 ◽  
Author(s):  
Daniel Andersen ◽  
Voicu Popescu
Keyword(s):  
Image Based Modeling ◽  
Indoor Scenes

Abstraction and Understanding of Indoor Scenes from Single-View RGB-D Scanning Data

2018 ◽  
Vol 30 (6) ◽  
pp. 1046
Author(s):  
Yuliang Sun ◽  
Yongwei Miao ◽  
Lijie Yu ◽  
Pajarola Renato
Keyword(s):  
Single View ◽  
Indoor Scenes

Segmentation for Indoor Scenes Based on DBSCAN Clustering

2019 ◽  
Vol 31 (7) ◽  
pp. 1183
Author(s):  
Mengdi Liu ◽  
Xiao Pan ◽  
Shanshan Gao ◽  
Shiqing Xin ◽  
Yuanfeng Zhou
Keyword(s):  
Dbscan Clustering ◽  
Indoor Scenes

SiFi

2021 ◽  
Vol 2 (3) ◽  
pp. 1-21
Author(s):  
Deke Guo ◽  
Xiaoqiang Teng ◽  
Yulan Guo ◽  
Xiaolei Zhou ◽  
Zhong Liu
Keyword(s):  
Rapid Development ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Location Based Services ◽  
Shopping Mall ◽  
Inference Problem ◽  
Indoor Location ◽  
Label Semantics ◽  
Geometrical Constraints ◽  
Indoor Scenes

Due to the rapid development of indoor location-based services, automatically deriving an indoor semantic floorplan becomes a highly promising technique for ubiquitous applications. To make an indoor semantic floorplan fully practical, it is essential to handle the dynamics of semantic information. Despite several methods proposed for automatic construction and semantic labeling of indoor floorplans, this problem has not been well studied and remains open. In this article, we present a system called SiFi to provide accurate and automatic self-updating service. It updates semantics with instant videos acquired by mobile devices in indoor scenes. First, a crowdsourced-based task model is designed to attract users to contribute semantic-rich videos. Second, we use the maximum likelihood estimation method to solve the text inferring problem as the sequential relationship of texts provides additional geometrical constraints. Finally, we formulate the semantic update as an inference problem to accurately label semantics at correct locations on the indoor floorplans. Extensive experiments have been conducted across 9 weeks in a shopping mall with more than 250 stores. Experimental results show that SiFi achieves 84.5% accuracy of semantic update.

scholarly journals Semantic Mapping for Mobile Robots in Indoor Scenes: A Survey

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 92
Author(s):  
Xiaoning Han ◽  
Shuailong Li ◽  
Xiaohui Wang ◽  
Weijia Zhou
Keyword(s):  
Mobile Robots ◽  
Semantic Information ◽  
Semantic Mapping ◽  
Human Beings ◽  
Future Directions ◽  
Essential Requirement ◽  
Semantic Map ◽  
Semantic Maps ◽  
Great Progress ◽  
Indoor Scenes

Sensing and mapping its surroundings is an essential requirement for a mobile robot. Geometric maps endow robots with the capacity of basic tasks, e.g., navigation. To co-exist with human beings in indoor scenes, the need to attach semantic information to a geometric map, which is called a semantic map, has been realized in the last two decades. A semantic map can help robots to behave in human rules, plan and perform advanced tasks, and communicate with humans on the conceptual level. This survey reviews methods about semantic mapping in indoor scenes. To begin with, we answered the question, what is a semantic map for mobile robots, by its definitions. After that, we reviewed works about each of the three modules of semantic mapping, i.e., spatial mapping, acquisition of semantic information, and map representation, respectively. Finally, though great progress has been made, there is a long way to implement semantic maps in advanced tasks for robots, thus challenges and potential future directions are discussed before a conclusion at last.

scholarly journals Flexible time course of spatial frequency use during scene categorization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sandro L. Wiesmann ◽  
Laurent Caplette ◽  
Verena Willenbockel ◽  
Frédéric Gosselin ◽  
Melissa L.-H. Võ
Keyword(s):  
Time Course ◽  
Task Characteristics ◽  
Scene Categorization ◽  
Fine Grained ◽  
Spatial Frequencies ◽  
Outdoor Scenes ◽  
Indoor Scenes ◽  
Fine Pattern ◽  
Late Stages ◽  
Basic Level

AbstractHuman observers can quickly and accurately categorize scenes. This remarkable ability is related to the usage of information at different spatial frequencies (SFs) following a coarse-to-fine pattern: Low SFs, conveying coarse layout information, are thought to be used earlier than high SFs, representing more fine-grained information. Alternatives to this pattern have rarely been considered. Here, we probed all possible SF usage strategies randomly with high resolution in both the SF and time dimensions at two categorization levels. We show that correct basic-level categorizations of indoor scenes are linked to the sampling of relatively high SFs, whereas correct outdoor scene categorizations are predicted by an early use of high SFs and a later use of low SFs (fine-to-coarse pattern of SF usage). Superordinate-level categorizations (indoor vs. outdoor scenes) rely on lower SFs early on, followed by a shift to higher SFs and a subsequent shift back to lower SFs in late stages. In summary, our results show no consistent pattern of SF usage across tasks and only partially replicate the diagnostic SFs found in previous studies. We therefore propose that SF sampling strategies of observers differ with varying stimulus and task characteristics, thus favouring the notion of flexible SF usage.

Real-time people counting for indoor scenes

2016 ◽  
Vol 124 ◽  
pp. 27-35 ◽  
Author(s):  
Jun Luo ◽  
Jinqiao Wang ◽  
Huazhong Xu ◽  
Hanqing Lu
Keyword(s):  
Real Time ◽  
People Counting ◽  
Indoor Scenes
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