# Efficient Global Optimization of Non-differentiable, Symmetric Objectives for Multi Camera Placement

arXiv: https://arxiv.org/abs/2103.11210 · HTML: https://mlsanity.com/paper/2103.11210/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 78 references that are in the map.
- First posted on arXiv 20 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [A Survey on Content-Aware Video Analysis for Sports](https://mlsanity.com/paper/1703.01170/) (2017)
- [SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization](https://mlsanity.com/paper/1502.02268/) (2015)
- [Approximation Algorithms for Hard Capacitated k-facility Location Problems](https://mlsanity.com/paper/1311.4759/) (2013)

## Built on by (most cited first, 2 of 2)

- [Know your sensORs -- A Modality Study For Surgical Action Classification](https://mlsanity.com/paper/2203.08674/) (2022)
- [An Empirical Study on Ensemble-Based Transfer Learning Bayesian Optimisation with Mixed Variable Types](https://mlsanity.com/paper/2601.15640/) (2026)

## Abstract (arXiv)

We propose a novel iterative method for optimally placing and orienting multiple cameras in a 3D scene. Sample applications include improving the accuracy of 3D reconstruction, maximizing the covered area for surveillance, or improving the coverage in multi-viewpoint pedestrian tracking. Our algorithm is based on a block-coordinate ascent combined with a surrogate function and an exclusion area technique. This allows to flexibly handle difficult objective functions that are often expensive and quantized or non-differentiable. The solver is globally convergent and easily parallelizable. We show how to accelerate the optimization by exploiting special properties of the objective function, such as symmetry. Additionally, we discuss the trade-off between non-optimal stationary points and the cost reduction when optimizing the viewpoints consecutively.