Efficient 3D Reconstruction in Noisy Agricultural Environments: A Bayesian Optimization Perspective for View Planning
Athanasios Bacharis, Konstantinos Polyzos, Henry J. Nelson, Georgios B. Giannakis, Nikos Papanikolopoulos
AI summary
Problem
Existing view planning methods for 3D reconstruction ignore environmental noise and typically rely on discretized search spaces or large camera arrays, leading to inefficient and inaccurate monitoring in real-world agricultural settings.
Approach
The authors formulate a geometric-based reconstruction quality function that accounts for unknown environmental noise and optimize it using an adaptive ensemble Gaussian process Bayesian optimization algorithm that continuously learns the best kernel and acquisition strategy.
Key results
- First to incorporate environmental noise into the view planning reward function
- Adaptive ensemble Gaussian process Bayesian optimization for continuous view selection
- Eliminates search space discretization by solving a fully continuous optimization problem
- Superior reconstruction accuracy with fewer cameras across simulated and real cornfield tests
Why it matters
Enables cost-effective, real-time agricultural monitoring and precision farming by maximizing 3D reconstruction quality with minimal hardware and computational overhead.
Abstract
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. While this task can be carried out using a large number of arbitrarily taken 2D images, their processing may become laborious, time-consuming, and in some instances may not provide the necessary information about the object of interest. An efficient alternative is the so-termed view planning (VP), which aims to optimally place a certain number of cameras in positions that maximize the visual information. 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 simulated and real noisy agricultural environments showcase the merits of the proposed VP approach for efficient 3D reconstruction with even a small number of available cameras.