Neural Observation Field Guided Hybrid Optimization of Camera Placement

TL;DR

The paper tackles efficient camera placement for multi-camera systems where visibility is non-differentiable and optimization is high-dimensional. It introduces a neural observation field, a differentiable, implicit representation that encodes scene priors and per-voxel observation metrics $(c,oldsymbol{}^{cc},oldsymbol{}^{co})$ to drive gradient-based optimization, while a non-gradient-based branch performs elite resampling to escape local optima. The resulting hybrid optimization achieves state-of-the-art performance on 2D, 3D, and room-scale datasets with about an 8x reduction in computation time, and is validated on a real-world capture system showing robustness to environmental noise. Key contributions include the neural observation field, the cooperative hybrid optimization framework, and comprehensive real-world validation, highlighting practical impact for VR, autonomous driving, and 3D reconstruction tasks.

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.

Figures (7)

• Figure 1: We introduce a hybrid optimization method based on the neural observation field for camera placement estimation. The target objects are represented by neural observation fields, which are compatible with any type of objects.
• Figure 2: Method overview. Our method takes target object $\mathcal{S}$ and initial camera placement $\{ \mathcal{P}_0, \mathcal{P}_1,...,\mathcal{P}_n \}$ as inputs to construct neural observation field $\mathcal{F}$. We then utilize the non-gradient-based optimization techniques along with gradient-based optimization methods for camera placement refinement. Throughout this optimization process, the neural observation field is continually updated (refer to section \ref{['algo:hcpo']}), until the termination criteria are met.
• Figure 3: Illustration of observation attribute $\mathbf{o}$ elements: Coverage $c$ in row (A), Camera-to-camera angle in row (B) and Camera-to-object angle in row (C). Only the third column satisfies our visibility condition.
• Figure 4: Camera placement control system. This system is able to control six 4K RGB cameras in SE(3) with a uniform distributed lighting source. Real images captured by optimized cameras.
• Figure 5: The robustness and time cost of our algorithm, we optimize 10 sets of camera placements with different initializations. The solid line is the mean value, with the shading represents the upper and lower bounds. Additionally, we have tested the time cost of different parts in our method.