  3D reconstruction is a fundamental task in robotics that gained attention due
to its major impact in a wide variety of practical settings, including
agriculture, underwater, and urban environments. This task can be carried out
via view planning (VP), which aims to optimally place a certain number of
cameras in positions that maximize the visual information, improving the
resulting 3D reconstruction. Nonetheless, in most real-world settings, existing
environmental noise can significantly affect the performance of 3D
reconstruction. To that end, this work advocates a novel geometric-based
reconstruction quality function for VP, that accounts for the existing noise of
the environment, without requiring its closed-form expression. With no analytic
expression of the objective function, this work puts forth an adaptive Bayesian
optimization algorithm for accurate 3D reconstruction in the presence of noise.
Numerical tests on noisy agricultural environments showcase the merits of the
proposed approach for 3D reconstruction with even a small number of available
cameras.
