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Adaptive façades represent a viable and effective technological solution to reduce the building energy demand for cooling while achieving interesting aesthetic effects on the building envelope to screen solar radiation. During the last decade, many different design solutions, including those based on shape memory alloys, have been experimented to obtain appropriate responses without being dependent on electro-mechanically actuated systems. Several recent and ongoing studies have been published in the scientific literature regarding the different actuator typologies, as well as the different properties of the materials used, which usually determine the adaptive solution characteristics after a series of complex and time-consuming simulations using specialised dynamic modelling software. Due to the time and resources required, this kind of evaluation is usually delivered during the last and more advanced design stage as a form of assessment of already-taken architectural and technological choices. The study reported in the paper aims to offer a quick, time-saving simplified algorithm to calculate the response of an adaptive façade, according to the ISO 13790 standards, to be adopted during the early design stage to evaluate the possible effects of design decisions. The study includes three main steps: (a) the conceptualisation of the adaptive solution considering the context conditions; (b) the definition of the calculation algorithm; (c) the application of the method to a test room in a case study building located in Bologna for supporting the discussion of the related outcomes. [ABSTRACT FROM AUTHOR]

Recognizing 3D objects and estimating their postures in a complex scene is a challenging task. Sample Consensus Initial Alignment (SAC-IA) is a commonly used point cloud-based method to achieve such a goal. However, its efficiency is low, and it cannot be applied in real-time applications. This paper analyzes the most time-consuming part of the SAC-IA algorithm: sample generation and evaluation. We propose two improvements to increase efficiency. In the initial aligning stage, instead of sampling the key points, the correspondence pairs between model and scene key points are generated in advance and chosen in each iteration, which reduces the redundant correspondence search operations; a geometric filter is proposed to prevent the invalid samples to the evaluation process, which is the most time-consuming operation because it requires transforming and calculating the distance between two point clouds. The introduction of the geometric filter can significantly increase the sample quality and reduce the required sample numbers. Experiments are performed on our own datasets captured by Kinect v2 Camera and on Bologna 1 dataset. The results show that the proposed method can significantly increase (10–30×) the efficiency of the original SAC-IA method without sacrificing accuracy. [ABSTRACT FROM AUTHOR]