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.

本文介绍了 Photo-SLAM，一种具有超级基元地图的创新 SLAM 框架，通过同时利用显式几何特征进行定位和学习隐式光度特征来表示观察环境的纹理信息，以及采用高斯金字塔训练方法逐步学习多级特征以增强逼真的映射性能。实验证明，与当前最先进的 SLAM 系统相比，我们提出的 Photo-SLAM 在线逼真映射的性能显著优于其他系统，如 Replica 数据集中的 PSNR 提高了 30％，渲染速度快了数百倍。此外，Photo-SLAM 可以在像 Jetson AGX Orin 这样的嵌入式平台上实时运行，展示了机器人应用的潜力。