  Exploring the most task-friendly camera setting -- optimal camera placement
(OCP) problem -- in tasks that use multiple cameras is of great importance.
However, few existing OCP solutions specialize in depth observation of indoor
scenes, and most versatile solutions work offline. To this problem, an OCP
online solution to depth observation of indoor scenes based on reinforcement
learning is proposed in this paper. The proposed solution comprises a
simulation environment that implements scene observation and reward estimation
using shadow maps and an agent network containing a soft actor-critic
(SAC)-based reinforcement learning backbone and a feature extractor to extract
features from the observed point cloud layer-by-layer. Comparative experiments
with two state-of-the-art optimization-based offline methods are conducted. The
experimental results indicate that the proposed system outperforms seven out of
ten test scenes in obtaining lower depth observation error. The total error in
all test scenes is also less than 90% of the baseline ones. Therefore, the
proposed system is more competent for depth camera placement in scenarios where
there is no prior knowledge of the scenes or where a lower depth observation
error is the main objective.

Computer Vision and Pattern Recognition;Machine Learning