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The rooftop is a default location for photovoltaic solar panels and is often not enough to offset increasing building energy consumption. The vertical surface of urban buildings offers a prime location to harness solar energy. The overall goal of this research is to evaluate power production potentials and multi-functionalities of a 3D building integrated photovoltaic (BIPV) facade system. The traditional BIPV which is laminated with window glass obscures the view-out and limits daylight penetration. Unlike the traditional system, the 3D solar module was configured to reflect the sun path geometry to maximize year-round solar exposure and energy production. In addition, the 3D BIPV façade offers multiple functionalities – solar regulations, daylighting penetration, and view-out, resulting in energy savings from heating, cooling, and artificial lighting load. Its ability to produce solar energy offsets building energy consumption and contributes to net-zero-energy buildings. Both solar simulations and physical prototyping were carried out to investigate the promises and challenges of the 3D BIPV façade system compared to a traditional BIPV system. With climate emergency on the rise and the need for clean, sustainable energy becoming ever more pressing, the 3D BIPV façade in this paper offers a creative approach to tackling the problems of power production, building energy savings, and user health and wellbeing. 

Acquiring accurate 3D biological models efficiently and economically is important for morphological data collection and analysis in organismal biology. In recent years, structure-from-motion (SFM) photogrammetry has become increasingly popular in biological research due to its flexibility and being relatively low cost. SFM photogrammetry registers 2D images for reconstructing camera positions as the basis for 3D modeling and texturing. However, most studies of organismal biology still relied on commercial software to reconstruct the 3D model from photographs, which impeded the adoption of this workflow in our field due the blocking issues such as cost and affordability. Also, prior investigations in photogrammetry did not sufficiently assess the geometric accuracy of the models reconstructed. Consequently, this study has two goals. First, we presented an affordable and highly flexible SFM photogrammetry pipeline based on the open-source package OpenDroneMap (ODM) and its user interface WebODM. Second, we assessed the geometric accuracy of the photogrammetric models acquired from the ODM pipeline by comparing them to the models acquired via microCT scanning, the de facto method to image skeleton. Our sample comprised 15 Aplodontia rufa (mountain beaver) skulls. Using models derived from microCT scans of the samples as reference, our results showed that the geometry of the models derived from ODM was sufficiently accurate for gross metric and morphometric analysis as the measurement errors are usually around or below 2%, and morphometric analysis captured consistent patterns of shape variations in both modalities. However, subtle but distinct differences between the photogrammetric and microCT-derived 3D models could affect the landmark placement, which in return affected the downstream shape analysis, especially when the variance within a sample is relatively small. At the minimum, we strongly advise not combining 3D models derived from these two modalities for geometric morphometric analysis. Our findings can be indictive of similar issues in other SFM photogrammetry tools since the underlying pipelines are similar. We recommend that users run a pilot test of geometric accuracy before using photogrammetric models for morphometric analysis. For the research community, we provide detailed guidance on using our pipeline for building 3D models from photographs.

 

Abstract Differentiable rendering of translucent objects with respect to their shapes has been a long‐standing problem. State‐of‐the‐art methods require detecting object silhouettes or specifying change rates inside translucent objects—both of which can be expensive for translucent objects with complex shapes. In this paper, we address this problem for translucent objects with no refractive or reflective boundaries. By reparameterizing interior components of differential path integrals, our new formulation does not require change rates to be specified in the interior of objects. Further, we introduce new Monte Carlo estimators based on this formulation that do not require explicit detection of object silhouettes.