Lighting ambiances and materialities of wood in architecture : a comparative evaluation of the quality of spaces in relation to interior finishes

Le bois est un matériau souvent utilisé par les architectes pour améliorer l'ambiance générale d'un espace, mais peu de recherches en présentent l’impact réel du matériau sur les impressions visuelles et les effets lumineux. Cette recherche étudie l'influence de la matérialité du bois par rapport à la création d'ambiances d'éclairage spécifiques dans l'architecture. Plus particulièrement, elle se concentre sur l'impact des panneaux décoratifs en bois à générer de la diversité lumineuse dans les espaces intérieurs et son potentiel à améliorer la satisfaction environnementale et l'efficacité énergétique. La recherche utilise des maquettes à l’échelle pour leur précision dans la représentation des ambiances lumineuses d’espaces éclairés naturellement ainsi que les technologies récentes d'imagerie digitale pour capturer et analyser les résultats. La méthodologie permet la comparaison entre les différents réglages des espaces intérieurs créés par une sélection des types de matérialités du bois: la réflectance (valeur), la couleur et la réflectivité. Les modalités spatiales sont comparées en présence d’ensoleillement direct et sous des conditions de ciel couvert puisque les modèles d'éclairage et les ambiances diffèrent considérablement. Les résultats permettent d’établir une discussion sur les ambiances en termes de brillance et de contraste, sur la couleur ainsi que la répartition des zones lumineuses dans l'espace. La recherche souligne le rôle des matérialités que peuvent prendre le bois pour optimiser la diversité lumineuse et la création d'ambiances visuellement confortables, ainsi que ses possibilités d'améliorer les ambiances architecturales par rapport à la lumière.Wood is a material often used by architects to enhance the overall ambiance of a space but few research discuss its actual impact on visual impressions and luminous effects. This research studies the influence of wood materialities in relation to creating specific lighting ambiances in architecture. More particularly, it focuses on the impact of decorative wood indoor panels on the creation of daylighting diversity in interior spaces and the potential to improve environmental satisfaction and energy efficiency. The research uses scale models for their accuracy in rendering complex daylighting ambiances in conjunction with the latest imaging technologies to capture and analyze the results. The methodology enables the comparison between different settings of interior spaces created by a selection of wood type materialities: ratio (percentage), color and gloss. Spatial modalities are compared in the presence of direct sunlighting and diffuse skylight conditions since lighting patterns and ambiances differ considerably. The results enable a discussion of ambiances in terms of brightness and contrast, color, as well as the luminous distribution in the space. The research underlines roles of wood materialities to achieve luminous diversity and creating visually comfortable interior ambiances as well as its opportunities to enhance architectural ambiances in relation to light

Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction

The ultimate goal of many image-based modeling systems is to render photo-realistic novel views of a scene without visible artifacts. Existing evaluation metrics and benchmarks focus mainly on the geometric accuracy of the reconstructed model, which is, however, a poor predictor of visual accuracy. Furthermore, using only geometric accuracy by itself does not allow evaluating systems that either lack a geometric scene representation or utilize coarse proxy geometry. Examples include light field or image-based rendering systems. We propose a unified evaluation approach based on novel view prediction error that is able to analyze the visual quality of any method that can render novel views from input images. One of the key advantages of this approach is that it does not require ground truth geometry. This dramatically simplifies the creation of test datasets and benchmarks. It also allows us to evaluate the quality of an unknown scene during the acquisition and reconstruction process, which is useful for acquisition planning. We evaluate our approach on a range of methods including standard geometry-plus-texture pipelines as well as image-based rendering techniques, compare it to existing geometry-based benchmarks, and demonstrate its utility for a range of use cases.Comment: 10 pages, 12 figures, paper was submitted to ACM Transactions on Graphics for revie

Multiview 3D reconstruction in geosciences

Multiview three-dimensional (3D) reconstruction is a technology that allows the creation of 3D models of a given scenario from a series of overlapping pictures taken using consumer-grade digital cameras. This type of 3D reconstruction is facilitated by freely available software, which does not require expert-level skills. This technology provides a 3D working environment, which integrates sample/field data visualization and measurement tools. In this study, we test the potential of this method for 3D reconstruction of decimeter-scale objects of geological interest. We generated 3D models of three different outcrops exposed in a marble quarry and two solids: a volcanic bomb and a stalagmite. Comparison of the models obtained in this study using the presented method with those obtained using a precise laser scanner shows that multiview 3D reconstruction yields models that present a root mean square error/average linear dimensions between 0.11 and 0.68%. Thus this technology turns out to be an extremely promising tool, which can be fruitfully applied in geosciences

An iterative algorithmic UAV path optimization process for Structure-for-Motion modelling

The use of unmanned aerial vehicles (UAVs) for 3D reconstruction through photogrammetry has gained significant attention in recent years. With the advancement of technology and the availability of affordable drones with high-resolution cameras, capturing aerial images for creating detailed 3D models has become more accessible, however, UAV survey flight planning still presents challenges. The planning stage is essential in aerial photogrammetry as it sets the foundation for efficient and accurate surveying. Proper predictive planning ensures a smooth workflow on site, generating high-quality datasets for reconstruction while minimizing the need for repeat surveys. This approach not only reduces costs but also mitigates potential errors and delays during the survey process. Within the presented frame of reference, the present study explores the use of UAVs for 3D reconstruction through photogrammetry, focusing on optimizing flight paths and view planning. It addresses challenges such as safety, navigation, and image dataset optimization. The study presents the current advancement of custom parametric workflow developed in Rhino/Grasshopper. The workflow is targeted for average users, aiming to simplify the process and integrate it with architectural and planning workflows. The approach involves four algorithms, including proxy model generation, visibility analysis, path generation, and camera position estimation. The iterative process enhances precision through progressive refinement of the proxy model, offering potential for predictive modelling and effective photogrammetry utilization in UAV planning. Further research and testing are needed to validate real-world performance

Electronic Imaging & the Visual Arts. EVA 2012 Florence

The key aim of this Event is to provide a forum for the user, supplier and scientific research communities to meet and exchange experiences, ideas and plans in the wide area of Culture & Technology. Participants receive up to date news on new EC and international arts computing & telecommunications initiatives as well as on Projects in the visual arts field, in archaeology and history. Working Groups and new Projects are promoted. Scientific and technical demonstrations are presented

Architectural Street Credibility: Reframing Contemporary Architecture to Sidewalk Level with Images from Google Street View

abstract: The purpose of this research was to assess the condition of the human/building interface at sidewalk level by reframing our view of contemporary architecture using Google Street View images. In particular, the goal was to find a means by which aesthetic engagement in the urban cultural ecology could be measured. Photo-elicitation, semantic differential, and visual assessment methods were adapted and combined to develop a photo-semantic assessment survey instrument for this study aimed at evaluating respondent preference for building images. Architectural adjective usage amongst 14 graduate students was surveyed, and the resulting 175-word list was synthesized down to seven positive and seven negative adjectives. Eleven representative buildings were selected from the Phaidon Atlas of 21st Century World Architecture, and photographic Street Views were created. The photo-semantic assessment survey instrument was administered to 62 graduate students given their demographic is reasonably similar to the urban walker stakeholder in the outcome. Respondent preference for the building images was then ranked ordered and correlations were run against various image factors including facade complexity, transparency, and streetscape quality. Moderate to strong correlations between preference and several image factors were observed indicating that certain building design factors, particularly facade complexity, may play a predictable role. Several avenues for future research are suggested including the comparison of lab versus on-site respondents; the comparison of user types including targeted, passerby and tourist; the effect of skyline on user preference for Street Views; and the effect of participation in the building making process on short and long term respondent preference.Dissertation/ThesisPh.D. Architecture 201

Large-Scale Mapping of Human Activity using Geo-Tagged Videos

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.Comment: Accepted at ACM SIGSPATIAL 201