Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular Stereo and RGB-D Cameras

Huajian Huang, Longwei Li, Hui Cheng, Sai-Kit Yeung; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 21584-21593

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. Project Page and code: https://huajianup.github.io/research/Photo-SLAM/.