  Many species face the threat of extinction. It's important to study these
species and gather information about them as much as possible to preserve
biodiversity. Due to the rarity of endangered species, there is a limited
amount of data available, making it difficult to apply data requiring
generative AI methods to this domain. We aim to study the feasibility of
generating consistent and real-like 3D models of endangered animals using
limited data. Such a phenomenon leads us to utilize zero-shot stable diffusion
models that can generate a 3D model out of a single image of the target
species. This paper investigates the intricate relationship between elevation
angle and the output quality of 3D model generation, focusing on the innovative
approach presented in DreamGaussian. DreamGaussian, a novel framework utilizing
Generative Gaussian Splatting along with novel mesh extraction and refinement
algorithms, serves as the focal point of our study. We conduct a comprehensive
analysis, analyzing the effect of varying elevation angles on DreamGaussian's
ability to reconstruct 3D scenes accurately. Through an empirical evaluation,
we demonstrate how changes in elevation angle impact the generated images'
spatial coherence, structural integrity, and perceptual realism. We observed
that giving a correct elevation angle with the input image significantly
affects the result of the generated 3D model. We hope this study to be
influential for the usability of AI to preserve endangered animals; while the
penultimate aim is to obtain a model that can output biologically consistent 3D
models via small samples, the qualitative interpretation of an existing
state-of-the-art model such as DreamGaussian will be a step forward in our
goal.
