What Looks Good with my Sofa: Ensemble Multimodal Search for Interior Design

Interior design is the core step of interior decoration, and it determines the overall layout and style of furniture. Traditional interior design is usually laborious and time‐consuming work carried out by professional designers and cannot always meet clients' personalized requirements. With the development of computer graphics, computer vision and machine learning, computer scientists have carried out much fruitful research work in computer‐aided personalized interior design (PID). In general, personalization research in interior design mainly focuses on furniture selection and floor plan preparation. In terms of the former, personalized furniture selection is achieved by selecting furniture that matches the resident's preference and style, while the latter allows the resident to personalize their floor plan design and planning. Finally, the automatic furniture layout task generates a stylistically matched and functionally complete furniture layout result based on the selected furniture and prepared floor plan. Therefore, the main challenge for PID is meeting residents' personalized requirements in terms of both furniture and floor plans. This paper answers the above question by reviewing recent progress in five separate but correlated areas, including furniture style analysis, furniture compatibility prediction, floor plan design, floor plan analysis and automatic furniture layout. For each topic, we review representative methods and compare and discuss their strengths and shortcomings. In addition, we collect and summarize public datasets related to PID and finally discuss its future research directions.

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“…Interior Items (2017) [TMS*17]: This dataset was published by Tautkute et al. for cross‐modal furniture style retrieval.…”

Section: Interior Design Related Datasetsmentioning

confidence: 99%

A Survey of Personalized Interior Design

Wang

Liang

Huai

et al. 2023

Computer Graphics Forum

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“…Salah satu data yang dikumpulkan yaitu contoh furniture yang digunakan sementara pada pengembangan aplikasi ini. Data-data furniture pada aplikasi didapatkan dari penelitian yang berjudul "What Looks Good with my Sofa: Multimodal Search Engine for Interior Design" (Tautkute, et al, 2017 (Nidhra & Dondeti, 2012). Dengan kata lain, pengujian black box yaitu pengujian yang dilakukan dengan menjalankan aplikasi pada lingkungan yang sebenarnya.…”

Section: Pengumpulan Dataunclassified

Implementasi Augmented Reality Furniture Dengan User-Defined Target Berbasis Android

Junaidi

Prabowo

Syarif

et al. 2020

J. Sistem Info. Bisnis

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Furniture catalogs display items statically in 2-dimensional form only. Buyers often want to observe furniture in a real room. Home-View application in this study can display 3D furniture objects as they want. Home-View App has been developed with Augmented Reality technology. Application is developed using the Vuforia SDK library and User Defined Target (UDT) method in the Unity3D software. UDT is utilized to display 3D furniture on a target like magazine and book covers. Home-View application has two menus namely Marker Katalog and HomeView. Marker Katalog serves as a method to view 3D furniture in a small form in the furniture catalog. HomeView serves as a way to view 3D furniture in the desired room. This application has a positioning feature on the x and y axis and rotating feature on the z axis. The application can run by displaying 3D furniture to its actual size at the HomeView menu and successfully displaying 3D furniture as listed on the catalog at the Marker Katalog menu.

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“…In this section, we present a baseline style search engine model introduced in [9], which is the basis for our current research. It is built on top of two single-modal modules.…”

Section: From Single To Multimodal Searchmentioning

confidence: 99%

“…To address the above-mentioned shortcomings of the currently available search engines, we propose a novel end-toend method that uses neural network architecture to model the joint multimodal space of database objects. This method is an extension of our previous work [9] that blended multimodal results. Although in this paper we focus mostly on the fashion items such as clothes, accessories and furniture, our search engine is in principle agnostic to object types and can be successfully applied in many other multimedia applications.…”

Section: Introductionmentioning

confidence: 97%

DeepStyle: Multimodal Search Engine for Fashion and Interior Design

Tautkute¹,

Trzciński²,

Skorupa³

et al. 2019

IEEE Access

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In this paper, we propose a multimodal search engine that combines visual and textual cues to retrieve items from a multimedia database aesthetically similar to the query. The goal of our engine is to enable intuitive retrieval of fashion merchandise such as clothes or furniture. Existing search engines treat textual input only as an additional source of information about the query image and do not correspond to the real-life scenario where the user looks for "the same shirt but of denim". Our novel method, dubbed DeepStyle, mitigates those shortcomings by using a joint neural network architecture to model contextual dependencies between features of different modalities. We prove the robustness of this approach on two different challenging datasets of fashion items and furniture where our DeepStyle engine outperforms baseline methods by 18-21% on the tested datasets. Our search engine is commercially deployed and available through a Web-based application.

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What Looks Good with my Sofa: Ensemble Multimodal Search for Interior Design

Cited by 12 publications

References 26 publications

A Survey of Personalized Interior Design

A Survey of Personalized Interior Design

Implementasi Augmented Reality Furniture Dengan User-Defined Target Berbasis Android

DeepStyle: Multimodal Search Engine for Fashion and Interior Design

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