I. Introduction
Due to the advancement in 3D scene capturing and rendering, point clouds have been a popular representation in emerging applications, such as metaverse, 3D telepresence, gaming, and virtual/augmented reality [1], [2], [3]. Recently, compression of point clouds has gained a significant attention from both academia and industry [4], [5] to enable the transmission of the captured dynamic 3D scenes or objects to a remote location. However, almost all the previous efforts on point cloud compression are focused on addressing the problem of how to reduce the bit rate given a reconstruction quality level (i.e., quantization parameter, QP), or improve the reconstruction quality at the same bit rate. Given a bit rate constraint in the bandwidth limited channel, finding the optimal QPs that can achieve maximal reconstruction quality is still an open issue in point cloud compression.