  In nature, the collective behavior of animals, such as flying birds is
dominated by the interactions between individuals of the same species. However,
the study of such behavior among the bird species is a complex process that
humans cannot perform using conventional visual observational techniques such
as focal sampling in nature. For social animals such as birds, the mechanism of
group formation can help ecologists understand the relationship between social
cues and their visual characteristics over time (e.g., pose and shape). But,
recovering the varying pose and shapes of flying birds is a highly challenging
problem. A widely-adopted solution to tackle this bottleneck is to extract the
pose and shape information from 2D image to 3D correspondence. Recent advances
in 3D vision have led to a number of impressive works on the 3D shape and pose
estimation, each with different pros and cons. To the best of our knowledge,
this work is the first attempt to provide an overview of recent advances in 3D
bird reconstruction based on monocular vision, give both computer vision and
biology researchers an overview of existing approaches, and compare their
characteristics.
