  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.
