Inverse lighting design using a coverage optimization strategy

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.

Section: Lighting Designmentioning

confidence: 99%

A Survey of Personalized Interior Design

Wang

Liang

Huai

et al. 2023

“…Illumination as a goal has been central to other forms of inverse design, such as inverse lighting optimization for luminaire placement, car headlight design and opening design. Notable methods in this genre include the works of Kawai et al [KPC93] for designing the illumination in a static environment, Schwarz and Wonka [SW14] on street light pose optimization, Gkaravelis and Papaioannou [GP16] on the population of interior environments with light sources and Mas et al [MMP18] on headlight reflector design.…”

Section: Related Workmentioning

confidence: 99%

Illumination‐Guided Furniture Layout Optimization

Vitsas

Παπαϊωάννου

Gkaravelis

et al. 2020

Lighting plays a very important role in interior design. However, in the specific problem of furniture layout recommendation, illumination has been either neglected or addressed with empirical or very simplified solutions. The effectiveness of a particular layout in its expected task performance can be greatly affected by daylighting and artificial illumination in a non‐trivial manner. In this paper, we introduce a robust method for furniture layout optimization guided by illumination constraints. The method takes into account all dominant light sources, such as sun light, skylighting and fixtures, while also being able to handle movable light emitters. For this task, the method introduces multiple generic illumination constraints and physically‐based light transport estimators, operating alongside typical geometric design guidelines, in a unified manner. We demonstrate how to produce furniture arrangements that comply with important safety, comfort and efficiency illumination criteria, such as glare suppression, under complex light‐environment interactions, which are very hard to handle using empirical or simplified models.

“…To solve the inverse lighting problem, many researchers proposed techniques that find the light setting to closely match the desired target image painted by the user [SDS∗93, SC07, GP16, OMSI07, PBMF07, LHH∗13]. Schwarz and Wonka [SW14] introduced a procedural method for modeling exterior lighting.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, research on generating lighting has received relatively less attention. Existing studies on lighting generation either ignored the characteristics of lighting as light sources considering them only as furniture [MSL∗11], or ignored its relationship with other furniture considering only the functional aspect as the light source [GP16]. However, an appropriate lighting layout maintains a proper spatial relationship with other furniture and provides necessary illumination to the scene while satisfying the many constraints that depend on the characteristics of the 3D indoor scene and the furniture placed inside the scene.…”

Section: Introductionmentioning

confidence: 99%

Lighting Layout Optimization for 3D Indoor Scenes

Jin

Lee

2019

As the number of models for 3D indoor scenes are increasing rapidly, methods for generating the lighting layout have also become increasingly important. This paper presents a novel method that creates optimal placements and intensities of a set of lights in indoor scenes. Our method is characterized by designing the objective functions for the optimization based on the lighting guidelines used in the interior design field. Specifically, to apply major elements of the lighting guideline, we identify three criteria, namely the structure, function, and aesthetics, that are suitable for the virtual space and quantify them through a set of objective terms: pairwise relation, hierarchy, circulation, illuminance, and collision. Given an indoor scene with properly arranged furniture as input, our method combines the procedural and optimization‐based approaches to generate lighting layouts appropriate to the geometric and functional characteristics of the input scene. The effectiveness of our method is demonstrated with an ablation study of cost terms for the optimization and a user study for perceptual evaluation.