Multiview camera setups have proven useful in many computer vision applications for reducing ambiguities, mitigating occlusions, and increasing field-of-view coverage. However, the high computational cost associated with multiple views poses a significant challenge for end devices with limited computational resources. To address this issue, we propose a view selection approach that analyzes the target object or scenario from given views and selects the next best view for processing. Our approach features a reinforcement learning based camera selection module, MVSelect, that not only selects views but also facilitates joint training with the task network. Experimental results on multiview classification and detection tasks show that our approach achieves promising performance while using only 2 or 3 out of N available views, significantly reducing computational costs. Furthermore, analysis on the selected views reveals that certain cameras can be shut off with minimal performance impact, shedding light on future camera layout optimization for multiview systems. Code is available at https://github.com/hou-yz/MVSelect.

翻译：多相机设置在计算机视觉的许多应用中已被证明能够有效减少歧义、缓解遮挡并扩大视野覆盖范围。然而，多视角带来的高计算成本对计算资源有限的终端设备构成了重大挑战。为解决这一问题，我们提出了一种视角选择方法，该方法分析给定视角下的目标对象或场景，并选择下一个最佳视角进行处理。我们的方法包含一个基于强化学习的相机选择模块MVSelect，该模块不仅能选择视角，还能促进与任务网络的联合训练。在多视角分类和检测任务上的实验结果表明，我们的方法在使用N个可用视角中仅2或3个时，仍能实现有前景的性能，同时显著降低计算成本。此外，对所选视角的分析显示，某些相机可以在对性能影响最小的情况下关闭，这为未来多视角系统的相机布局优化提供了启示。代码可在https://github.com/hou-yz/MVSelect获取。