Neural Observation Field Guided Hybrid Optimization of Camera Placement

TL;DR

This paper introduces a hybrid optimization method for camera placement that combines gradient-based and non-gradient-based techniques using a neural observation field, enabling efficient and robust placement in diverse scenarios.

Contribution

The paper proposes a neural observation field to bridge different optimization methods, improving camera placement efficiency and robustness across various environments.

Findings

Achieves state-of-the-art camera placement performance.

Requires 8x less computation time than existing methods.

Demonstrates robustness in real-world noisy environments.

Abstract

Camera placement is crutial in multi-camera systems such as virtual reality, autonomous driving, and high-quality reconstruction. The camera placement challenge lies in the nonlinear nature of high-dimensional parameters and the unavailability of gradients for target functions like coverage and visibility. Consequently, most existing methods tackle this challenge by leveraging non-gradient-based optimization methods.In this work, we present a hybrid camera placement optimization approach that incorporates both gradient-based and non-gradient-based optimization methods. This design allows our method to enjoy the advantages of smooth optimization convergence and robustness from gradient-based and non-gradient-based optimization, respectively. To bridge the two disparate optimization methods, we propose a neural observation field, which implicitly encodes the coverage and observation quality. The neural observation field provides the measurements of the camera observations and corresponding gradients without the assumption of target scenes, making our method applicable to diverse scenarios, including 2D planar shapes, 3D objects, and room-scale 3D scenes.Extensive experiments on diverse datasets demonstrate that our method achieves state-of-the-art performance, while requiring only a fraction (8x less) of the typical computation time. Furthermore, we conducted a real-world experiment using a custom-built capture system, confirming the resilience of our approach to real-world environmental noise.