Interior Scenes Research Articles

Interior Scene Coloring Design Model Combining Improved K-means and SAA

Due to technological progress and changes in people’s aesthetic standards, traditional design models need to be constantly broken through, seeking more efficient and accurate design methods. Seeking effective design models to improve design efficiency and prediction accuracy is an important task. Therefore, this study proposes an indoor scene coloring design model that combines improved K-means clustering and simulated annealing algorithm for this important task. Based on the analysis of indoor scene coloring, particle swarm optimization algorithm is used to optimize K-means clustering to achieve color classification. Combined with simulated annealing algorithm, adaptive adjustment of lighting conditions is achieved to enhance the naturalness and realism of coloring. These results confirmed that the proposed method had the highest average F-value, with an average F-value of 92.524 and 143.601 on both datasets, respectively. The average ARI values were 0.361 and 0.897, respectively. The designed algorithm performed the best and converged faster than other three. Therefore, the proposed method can effectively ensure the consistency between the distribution of data objects after clustering and the actual situation. For indoor scene coloring design, it has important practical significance and provides new possible paths for improving design efficiency and prediction accuracy.

Investigating the Impact of Combined Daylight and Electric Light on Human Perception of Indoor Spaces

People spend more than 90% of their time indoors, and, as such, improving indoor lighting quality can enhance their quality of life by positively influencing both physiological and psychological aspects. Numerous studies suggest that perceptions of a space vary depending on a number of lighting attributes present. Significant effort has been made across various fields to identify the spatial lighting conditions and attributes that impact human perception, although we lack studies that explore the impact of these conditions in tandem. This paper investigates how interior lighting conditions influence human impressions of room ambiance. The study examines 16 different interior scenes, considering factors such as sky condition (sunny vs. overcast), shading blinds position (open vs. closed), presence or absence of electric light (on vs. off), and Correlated Color Temperature (CCT) (2700 K, 4000 K, and 6500 K). The evaluation is conducted within an office environment at Carnegie Mellon University, using a combination of objective lighting metrics and subjective assessments. In total, 26 participants, consisting of 11 females, 14 males, and one undisclosed, aged between 18 and 50, evaluated the office ambiance under various lighting conditions using semantic differential scales. The analysis showed that the variation of blinds and CCT levels significantly influenced the participants’ impression of light. The study also identified statistically significant interactions between “blinds and CCT” and “blinds and sky” conditions, highlighting the combined influence of these variables on shaping indoor light impressions. This research offers valuable insights into the complex interplay of different lighting factors in shaping human perceptions, and underscores the importance of optimizing indoor lighting conditions for creating healthy and sustainable indoor environments.

Automated Reconstruction of Existing Building Interior Scene BIMs Using a Feature-Enhanced Point Transformer and an Octree

Building information models (BIMs) offer advantages, such as visualization and collaboration, making them widely used in the management of existing buildings. Currently, most BIMs for existing indoor spaces are manually created, consuming a significant amount of manpower and time, severely impacting the efficiency of building operations and maintenance management. To address this issue, this study proposes an automated reconstruction method for an indoor scene BIM based on a feature-enhanced point transformer and an octree. This method enhances the semantic segmentation performance of point clouds by using feature position encoding to strengthen the point transformer network. Subsequently, the data are partitioned into multiple segments using an octree, collecting the geometric and spatial information of individual objects in the indoor scene. Finally, the BIM is automatically reconstructed using Dynamo in Revit. The research results indicate that the proposed feature-enhanced point transformer algorithm achieves a high segmentation accuracy of 71.3% mIoU on the S3DIS dataset. The BIM automatically generated from the field point cloud data, when compared to the original data, has an average error of ±1.276 mm, demonstrating a good reconstruction quality. This method achieves the high-precision, automated reconstruction of the indoor BIM for existing buildings, avoiding extensive manual operations and promoting the application of BIMs for the maintenance processes of existing buildings.

Use of Smartphone Lidar Technology for Low-Cost 3D Building Documentation with iPhone 13 Pro: A Comparative Analysis of Mobile Scanning Applications

Laser scanning technology has long been the preferred method for capturing interior scenes in various industries. With a growing market, smaller and more affordable scanners have emerged, offering end products with sufficient accuracy. While not on par with professional scanners, Apple has made laser scanning technology accessible to users with the introduction of the new iPhone Pro models, democratizing 3D scanning. Thus, this study aimed to assess the performance of the iPhone’s lidar technology as a low-cost solution for building documentation. Four scanning applications were evaluated to determine the accuracy, precision, and user experience of the generated point clouds compared with a terrestrial laser scanner. The results reveal varying performances on the same device, highlighting the influence of software. Notably, there is room for improvement, particularly in tracking the device’s position through software solutions. As it stands, the technology is well suited for applications such as indoor navigation and the generation of quick floor plans in the context of building documentation.

Deep scene synthesis of Atlanta-world interiors from a single omnidirectional image.

We present a new data-driven approach for extracting geometric and structural information from a single spherical panorama of an interior scene, and for using this information to render the scene from novel points of view, enhancing 3D immersion in VR applications. The approach copes with the inherent ambiguities of single-image geometry estimation and novel view synthesis by focusing on the very common case of Atlanta-world interiors, bounded by horizontal floors and ceilings and vertical walls. Based on this prior, we introduce a novel end-to-end deep learning approach to jointly estimate the depth and the underlying room structure of the scene. The prior guides the design of the network and of novel domain-specific loss functions, shifting the major computational load on a training phase that exploits available large-scale synthetic panoramic imagery. An extremely lightweight network uses geometric and structural information to infer novel panoramic views from translated positions at interactive rates, from which perspective views matching head rotations are produced and upsampled to the display size. As a result, our method automatically produces new poses around the original camera at interactive rates, within a working area suitable for producing depth cues for VR applications, especially when using head-mounted displays connected to graphics servers. The extracted floor plan and 3D wall structure can also be used to support room exploration. The experimental results demonstrate that our method provides low-latency performance and improves over current state-of-the-art solutions in prediction accuracy on available commonly used indoor panoramic benchmarks.

INVESTIGATION ON THE USE OF NeRF FOR HERITAGE 3D DENSE RECONSTRUCTION FOR INTERIOR SPACES

Abstract. The concept of Neural Radiance Fields (NeRF) emerged in recent years as a method to create novel synthetic 3D viewpoints from a set of trained images. While it has several overlaps with conventional photogrammetry and especially multi-view stereo (MVS), its main point of interest is the capability to rapidly recreate objects in 3D. In this paper, we investigate the quality of point clouds generated by state-of-the-art NeRF in the context of interior spaces and compare them to four conventional MVS algorithms, of which two are commercial (Agisoft Metashape and Pix4D) and the other two open source (Patch-Match and Semi-Global Matching). Three synthetic datasets of interior scenes were created from laser scanning data with different characteristics and architectural elements. Results show that NeRF point clouds could achieve satisfactory results geometrically speaking, with an average standard deviation of 1.7 cm in interior cases where the scene dimension is roughly 25–50 m3 in volume. However, the level of noise on the point cloud, which was considered as out of tolerance, ranges between 17–42%, meaning that the level of detail and finesse is most likely insufficient for sophisticated heritage documentation purposes, even though from a visualisation point of view the results were better. However, NeRF did show the capability to reconstruct texture less and reflective surfaces where MVS failed.

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the performance of eleven commonly used saliency models that combine traditional and deep learning methods on 126 rendered interior scenes with associated head tracking data. The data was extracted from three experiments conducted in virtual reality between 2016 and 2018. Two of these datasets pertain to the perceptual effects of daylight and include variations of daylighting conditions for a limited set of interior spaces, thereby allowing to test the influence of light conditions on human head movement. Ground truth maps were extracted from the collected head tracking logs, and the prediction accuracy of the models was tested via the correlation coefficient between ground truth and prediction maps. To address the possible inflation of results due to the equator bias, we conducted complementary analyses by restricting the area of investigation to the equatorial image regions. Although limited to immersive virtual environments, the promising performance of some traditional models such as GBVS360eq and BMS360eq for colored and textured architectural rendered spaces offers us the prospect of their possible integration into design tools. We also observed a strong correlation in head movements for the same space lit by different types of sky, a finding whose generalization requires further investigations based on datasets more specifically developed to address this question.

Data-driven Digital Lighting Design for Residential Indoor Spaces

Conventionally, interior lighting design is technically complex yet challenging and requires professional knowledge and aesthetic disciplines of designers. This article presents a new digital lighting design framework for virtual interior scenes, which allows novice users to automatically obtain lighting layouts and interior rendering images with visually pleasing lighting effects. The proposed framework utilizes neural networks to retrieve and learn underlying design guidelines and the principles beneath the existing lighting designs, e.g., a newly constructed dataset of 6k 3D interior scenes from professional designers with dense annotations of lights. With a 3D furniture-populated indoor scene as the input, the framework takes two stages to perform lighting design: (1) lights are iteratively placed in the room; (2) the colors and intensities of the lights are optimized by an adversarial scheme, resulting in lighting designs with aesthetic lighting effects. Quantitative and qualitative experiments show that the proposed framework effectively learns the guidelines and principles and generates lighting designs that are preferred over the rule-based baseline and comparable to those of professional human designers.

Interior Individual Trajectory Simulation with Population Distribution Constraint

Individual trajectory generation plays an important role in simulation tasks, reconstructing fine-grained mobility behaviors that can be used to evaluate epidemic risks, congestion risks, or commercial profit. Previous research works adopt the Newton’s mechanic-based particle model as their core algorithm, such as the Social Force model. However, real-world human mobility behaviors hardly follow the particle models, especially in the interior scenes where interactions between pedestrians and environments matter. In this article, we propose a Social Force-based trajectory simulator for interior scenarios that improve both trajectory quality and generation speed for interior scenarios. First, we introduce prior scene knowledge to guide the generation process, where pedestrians are armed with exploration behaviors that follow the group-level distribution. It provides more flexibility to simulate complicated human behaviors rather than straight-line movements, generating high-quality individual trajectories. Experiments show that the correlation between the aggregated population distribution of generated trajectories and ground-truth distribution is improved by 11.84% by our method. Second, we optimize the algorithm procedure by introducing a caching mechanism for tenderized intermediate values, along with graph-processing-unit-based implementation. Compared with the baseline Social Force model, we reduced the time consumption by 95%. More importantly, based on our simulation paradigm, we quantitatively evaluate several common mobility interventions in our simulation scenario, which can shed light on better policy designs in public spaces.

Creative and Progressive Interior Color Design with Eye-tracked User Preference

Interior scene colorization is vastly demanded in areas such as personalized architecture design. Existing works either require manual efforts to colorize individual objects or conform to fixed color patterns automatically learned from prior knowledge, whilst neglecting user preference. Quantitatively identifying user preferences is challenging, particularly at the early stage of the design process. The 3D setup also presents new challenges as the inhabitant can observe from any possible viewpoint. We propose a representative view selection method based on visual attention and a progressive preference inference model. We particularly focus on the progressive integration of eye-tracked user preference, which enables the assistance in creativity support and allows the possibility of convergent thinking. A series of user studies have been conducted to validate the effectiveness of the proposed view selection method, preference inference model and the creativity support mechanism.

Identifying Indoor Objects Using Neutrosophic Reasoning for Mobility Assisting Visually Impaired People

Indoor object detection is a fundamental activity for the development of applications of mobility-assistive technology for visually impaired people (VIP). The challenge of seeing interior objects in a real indoor environment is a challenging one since there are numerous complicated issues that need to be taken into consideration, such as the complexity of the background, occlusions, and viewpoint shifts. Electronic travel aids that are composed of the necessary sensors may assist VIPs with their navigation. The sensors have the ability to detect any obstacles, regardless of whether they are static or dynamic, and offer information on the context of an interior scene. The characteristics of an interior scene are not very clear and are subject to a great deal of variation. Recent years have seen the emergence of methods for dealing with issues of this kind, some of which include the use of neural networks, probabilistic methods, and fuzzy logic. This study describes a method for detecting indoor objects using a rotational ultrasonic array and neutrosophic logic. A neutrosophic set has been seen as the next evolution of the fuzzy set because of its indeterminate membership value, which is absent from conventional fuzzy sets. The suggested method is constructed to reflect the position of the walls (obstacle distance) and to direct the VIP to move freely (ahead, to the right, or to the left) depending on the degree of truthiness, the degree of indeterminacy, and the degree of falsity for the reflected distance. The results of the experiments show that the suggested indoor object detecting system has good performance, as its accuracy rate (a mean average precision) is 97.2 ± 1%.

Color enhancement processing of interior scene design images based on guided filtering

Color enhancement processing of interior scene design images based on guided filtering

Application and Practice of Artificial Intelligence Technology in Interior Design

Abstract In order to enhance the technical innovation of interior design, this paper proposes an interior design approach based on artificial intelligence technology to optimize the overall scheme of interior design. The shape and contour features of different kinds of objects in the interior are modeled by visual information design, the information components of the visual images are determined using correlation scale analysis, and the maximum grayscale values are marked to implement 3D visual feature reconstruction. The reconstruction process is based on accurately aligned point cloud data, and parametric surface reconstruction is implemented for the target objects to obtain a more accurate 3D interior design model and make the interior scene simulation more realistic. In order to verify the effectiveness of the application of the interior design method based on artificial intelligence technology, simulation design experiments are conducted. The results show that the multi-element fusion (TBG) consideration characteristics of the proposed method are more uniformly distributed. The visual saturation and stereo image design levels reach 98% and 88.51%, respectively. The average error result is 0.06, which are both better than the 3D vision-based interior design method and the multi-element fusion-based interior design method, and have higher design reliability. It can be seen that the interior design method based on artificial intelligence technology helps to enhance the effect of intelligent application of interior design and promotes the optimization and upgrading of the interior design working experience.

Subjective Impression of an Office with Biophilic Design and Blue Lighting: A Pilot Study

This paper investigates and compares people’s subjective impression of an office with a biophilic design and blue lighting. Existing studies have examined their influence on perception separately, but how they compare is unclear. Additionally, only a few studies have used an office setting as a case study. To address this research gap, this study collected people’s ratings and rankings of four simulated interior scenes of a private office using an online survey. The scenes include blue lighting, a biophilic design with daylight and view, a biophilic design with indoor plants, and a non-biophilic baseline with conventional white lighting. A total of 284 complete responses were collected and analyzed using a mixed-effect model. It was found that the two biophilic designs improved people’s perception of the office compared to the base case. The biophilic design with access to daylight and view outperformed the space with indoor plants in all the examined perceptual categories, specifically how the office space was perceived by participants as brighter, more comfortable, and spacious. On the contrary, the space with blue lighting decreased people’s ratings in most perceptual attributes in comparison to the baseline. The negative influence was notably significant in how lively, comfortable, bright, and appealing the space was perceived as being by participants. Subjects’ preference rankings of the four simulated office spaces showed a similar pattern.

Refabulating Domesticity

In the seventeenth century, Dutch artists like De Hooch, Vermeer, and de Witte famously painted interior scenes. These could seem to follow the birth of bourgeois domesticity and the modern sense of intimate, private dwelling. Still, many scholars have pointed out that this realism is deceptive: these interiors are in fact fabulated spaces, at odds with the architectural and social reality of their day. Following Eve Kosofsky Sedgwick’s opposition between a ‘paranoid’ and a ‘reparative’ reading of cultural artefacts, we could interpret these interiors in a paranoid mode, revealing the oppressive and exploitative social conditions of the so-called Golden Age in which the genre flourished. However, as Sedgwick suggests, such an interpretation can be supplemented by a reparative, more speculative, and creative hermeneutics that recontextualises these cultural artefacts. Such an approach can be aligned with the notion of ‘fictioning’ as developed by David Burrows and Simon O’Sullivan: the exploration of fictional worlds and other possible modes of existence they establish. While they mostly discuss rather outlandish forms of mythopoetic imagination, this text-based essay focuses on ‘fictioning’ the interior as a tool to experiment with the domestic. The essay will follow three artistic concepts: pictorial formalism (focusing on sensations), tropism (focusing on affects), and metalepsis (focusing on characters). In this essay, I explore the ‘impossible’ interior of Emanuel De Witte’s Interior with a Woman at the Virginal (c. 1665), an apparent idyllic scene that nonetheless generates a particularly unsettling atmosphere, as a case study. The painting consists of a complex constellation of places and stories; an assemblage of spaces, objects, and characters that resist their original pictorial and discursive frames. As such, it allows for a reparative, creative approach to the antiquated genre of the interior.

A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation.

Motion segmentation is one of the fundamental steps for detection, tracking, and recognition, and it can separate moving objects from the background. In this paper, we propose a spatial-motion-segmentation algorithm by fusing the events-dimensionality-preprocessing algorithm (EDPA) and the volume of warped events (VWE). The EDPA consists of depth estimation, linear interpolation, and coordinate normalization to obtain an extra dimension (Z) of events. The VWE is conducted by accumulating the warped events (i.e., motion compensation), and the iterative-clustering algorithm is introduced to maximize the contrast (i.e., variance) in the VWE. We established our datasets by utilizing the event-camera simulator (ESIM), which can simulate high-frame-rate videos that are decomposed into frames to generate a large amount of reliable events data. Exterior and interior scenes were segmented in the first part of the experiments. We present the sparrow search algorithm-based gradient ascent (SSA-Gradient Ascent). The SSA-Gradient Ascent, gradient ascent, and particle swarm optimization (PSO) were evaluated in the second part. In Motion Flow 1, the SSA-Gradient Ascent was 0.402% higher than the basic variance value, and 52.941% faster than the basic convergence rate. In Motion Flow 2, the SSA-Gradient Ascent still performed better than the others. The experimental results validate the feasibility of the proposed algorithm.

Furniture Style Compatibility Estimation by Multi-Branch Deep Siamese Network

As demands for understanding visual style among interior scenes increase, estimating style compatibility is becoming challenging. In particular, furniture styles are difficult to define due to their various elements, such as color and shape. As a result, furniture style is an ambiguous concept. To reduce ambiguity, Siamese networks have frequently been used to estimate style compatibility by adding various features that represent the style. However, it is still difficult to accurately represent a furniture’s style, even when using alternate features associated with the images. In this paper, we propose a new Siamese model that can learn from several furniture images simultaneously. Specifically, we propose a one-to-many ratio input method to maintain high performance even when inputs are ambiguous. We also propose a new metric for evaluating Siamese networks. The conventional metric, the area under the ROC curve (AUC), does not reveal the actual distance between styles. Therefore, the proposed metric quantitatively evaluates the distance between styles by using the distance between the embedding of each furniture image. Experiments show that the proposed model improved the AUC from 0.672 to 0.721 and outperformed the conventional Siamese model in terms of the proposed metric.

Beyond the Horizon

Beyond the Horizon

A Data-Driven Intelligent System for Assistive Design of Interior Environments.

This paper analyses the design of a healthy interior environment using big data intelligence. The application of big data intelligence in the design of healthy interior environments is necessary because the traditional interior design approaches consume a lot of energy and other problems. Benefited by its strong ability of computation and analytics, artificial intelligence can well improve a series of problems in the field of interior design. The proposal summarizes the sources, classifications, and expressions of behavioral data in interior spaces, carries out analysis and research on behavioral data from two aspects: display space and supermarket space, summarizes the interior methods based on behavioral data, and analyses the visualization application of behavioral data in different interior scenes, to explore the application value of behavioral data in interior design. In contrast to it is the unconscious behavioral response, the biggest characteristic of which is that it is regulated by the behavioral subject's physiological factors or habits of the behavior issuer. In this paper, we convert the layout recommendation problem of a space into a functional classification problem of segmented segments and household segments on a plane. The scene layout features are extracted by binary coding, the abstraction of the cross features between the vector segments is achieved by using a word embedding algorithm, the feature matrix is reduced in dimensionality, and finally, the segmentation network model and the layout network model are constructed, respectively, by using a bidirectional LSTM. The experiments show that the accuracy of the layout recommendation model in this paper is 98%, which can meet the demand for real-time online layouts.

Subjective and physiological responses towards daylit spaces with contemporary façade patterns in virtual reality: Influence of sky type, space function, and latitude

Recent studies have shown that the façade and light patterns in a space are an important factor in the occupants' experience, but little is known about the generalizability of these findings in different lighting conditions, uses of space, or latitudes. This study employed virtual reality to investigate the effects of façade geometry and daylight patterns on space impressions and physiological responses (skin conductance, heart rate, and heart rate variability), while simultaneously examining the effect of sky type, spatial context, and latitude on participant responses. In an experimental study conducted in Switzerland and Greece, 256 participants were exposed to immersive interior scenes with six façade geometry variations with the same perforation ratio, derived from contemporary architecture. Participants evaluated the scenes under different sky types (clear sky with high or low sun angle, or overcast sky) and spatial contexts (working or socializing). The façade geometry influenced both the appraisal (how pleasant, interesting, exciting, or calming the space was perceived) and the visual appearance of the space (how complex, bright, or spacious the space was perceived). Façade geometry also influenced the reported satisfaction with the amount of view, with a façade variation with small, irregularly distributed openings driving this effect. Neither the sky type nor the spatial context influenced space impressions. Results showed a significant effect of country and an interaction between country and façade geometry only for ratings of excitement, with participants in Greece rating specific façade variations as more exciting than participants in Switzerland. Skin conductance level (logΔSCL) decreased under exposure to a social context in clear sky with a low sun angle compared to a working context in overcast sky conditions. No significant effects were found for the other physiological measures. The results of the present study show that façade geometry was the main driver of the participants’ spatial experience, inducing perceptual effects that were robust to variations in sky type and space function and to regional differences (except for excitement). These findings have implications for the built environment, delineating the façade geometry as an important design tool with a high application potential across lighting conditions and space uses, and across latitudes between central and southern Europe.