A Model-Based Deep-Learning Approach to Reconstructing the Highly
Articulated Flight Kinematics of Bats
Abstract
Bats are capable of highly dexterous flight maneuvers that rely heavily
on highly articulated hand skeletons and malleable wing membranes. To
understand the underlying mechanisms, large amounts of detailed data on
bat flight kinematics are required. Conventional methods to obtain these
data have been based on tracing landmarks and require substantial manual
efforts. To generate 3D reconstructions of the entire geometry of a
flying bat in a fully automated fashion, the current work has developed
an approach where the pose of a trainable articulated mesh template that
is based on the bat’s anatomy is optimized to fit a set of binary
silhouette representing views from different directions of the flying
bat. This is followed by post-processing to smooth the reconstructed
kinematics and simulate the non-rigid motion of the wing membranes. To
evaluate the method, 10 flight sequences that represent several flight
maneuvers (e.g., straight flight, takeoff, u-turn) and were recorded in
a flight tunnel instrumented with 50 synchronized cameras have been
reconstructed. A total of 4,975 reconstructions are generated in this
fashion and subject to qualitative and quantitative evaluations with
promising results. The reconstructions are to be used for quantitative
analyses of the maneuvering kinematics and the associated aerodynamics.