A rapid and cost-effective pipeline for digitization of museum specimens with 3D photogrammetry

Abstract

Natural history collections are yielding more information as digitization brings specimen data to researchers, connects specimens across museums, and as new technologies allow for more large-scale data collection. Therefore, a key goal in specimen digitization is developing methods that both increase access and allow for the highest yield of phenomic data. 3D digitization is increasingly popular because it has the potential to meet both aspects of that key goal. However, current methods overlook or do not prioritize some of the most sought-after phenotypic traits, those involving the external appearance of specimens, especially color. Here, we introduce an efficient and cost-effective pipeline for 3D photogrammetry to capture the external appearance of natural history specimens and other museum objects. 3D photogrammetry aligns and compares sets of dozens, hundreds, or even thousands of photos to create 3D models. The hardware setup requires little physical space and around $3,000 in initial investment, while the software pipeline requires $1,400/year for proprietary software subscriptions (with open-source alternatives). The creation of each 3D model takes 1-2 hours/specimen and much of the software pipeline is automated with minimal supervision required, including the onerous step of mesh processing. We showcase the method by creating 3D models for most of the type specimens in the Moore Laboratory of Zoology bird collection and show that digital bill measurements are comparable to handtaken measurements. Color data, while not included as part of this pipeline, is easily extractable from the models and one of the most promising areas of data collection. Future advances can adapt the method for ultraviolet reflectance capture and increased efficiency and model quality. Combined with genomic data, phenomic data from 3D models including photogrammetry will open new doors to understanding organismal evolution.

Coastal Erosion of Arctic Cultural Heritage in Danger: A Case Study from Svalbard, Norway

“…TLS data were pre-processed in RiScanPro and exported as point clouds in .ptx file format. Further 3D modelling was carried out in the RealityCapture photogrammetry [44] software package allowing for the combined processing of TLS and image data. Listed buildings and other cultural heritage remains were exported as highresolution point clouds with an average point spacing between 0.5 mm and 2 mm for data visualisation and analysis within the Nubigon point cloud rendering software.…”

Web databases of feather photographs are useful tools for avian morphometry studies

“…Photogrammetry of digital images is used routinely in diverse areas of zoology and ecology (Breuer et al., 2007; Mendes et al., 2016; Munge & Athreya, 2020; Rycroft et al., 2013), its simplicity and precision having been demonstrated repeatedly (Ireland et al., 2006; Muñoz‐Muñoz & Perpiñán, 2010; Ortega‐Ortiz et al., 2018). Hence, bearing in mind the anticipated digitalization of museum collections (Medina et al., 2020) and the expected addition of new material to the databases, we may be confident that images from which to obtain precise data on feather dimensions and variability will be available in the medium term for a large proportion of avian species. In the meanwhile, the procedure presented here can be also applied with small errors to feathers directly measured from open‐wing preparations and bird round skins preserved in collections.…”

Morphology of migration: Associations between wing, and bill morphology and migration in kingbirds (Tyrannus)

“…For example, specific bill shapes could accommodate spatiotemporal changes in the competitive landscape (Navalón et al , 2019) or provide increased stability for prey capture in perch-gleaning or hover-gleaning foraging techniques (Fitzpatrick, 1985; Fitzpatrick et al , 2004; Botero-Delgadillo, 2011; Botero-Delgadillo & Bayly, 2012). The diversity of habitats that migratory taxa encounter during their annual cycle may shape phenotypes in more complex ways than previously thought, and techniques that incorporate more advanced characterizations of morphological variation may expose novel insight into how movement life history strategies shape phenotypes (e.g., Pol et al , 2009; Mallarino et al , 2011; Navalón et al , 2019; Alonso et al , 2020; Medina et al , 2020).…”