  The integration of neural rendering and the SLAM system recently showed
promising results in joint localization and photorealistic view reconstruction.
However, existing methods, fully relying on implicit representations, are so
resource-hungry that they cannot run on portable devices, which deviates from
the original intention of SLAM. In this paper, we present Photo-SLAM, a novel
SLAM framework with a hyper primitives map. Specifically, we simultaneously
exploit explicit geometric features for localization and learn implicit
photometric features to represent the texture information of the observed
environment. In addition to actively densifying hyper primitives based on
geometric features, we further introduce a Gaussian-Pyramid-based training
method to progressively learn multi-level features, enhancing photorealistic
mapping performance. The extensive experiments with monocular, stereo, and
RGB-D datasets prove that our proposed system Photo-SLAM significantly
outperforms current state-of-the-art SLAM systems for online photorealistic
mapping, e.g., PSNR is 30% higher and rendering speed is hundreds of times
faster in the Replica dataset. Moreover, the Photo-SLAM can run at real-time
speed using an embedded platform such as Jetson AGX Orin, showing the potential
of robotics applications.
