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.

翻译：三维重建是机器人领域的一项基础任务，因其在农业、水下及城市环境等多种实际场景中的重大影响而备受关注。该任务可通过视角规划实现，其目标是在最优位置放置一定数量的相机，以最大化视觉信息并提升最终的三维重建质量。然而，在多数真实环境中，现有噪声会显著影响三维重建的性能。为此，本文提出一种新颖的基于几何的重建质量函数用于视角规划，该函数在无需解析表达式的前提下，能够考虑环境中的现有噪声。针对目标函数无闭式解析表达的问题，本文进一步提出一种自适应贝叶斯优化算法，可在存在噪声的情况下实现精确的三维重建。在嘈杂农业环境中的数值实验表明，即便使用少量相机，所提方法在三维重建中仍具有显著优势。