@inproceedings{
lv2024sarslam,
title={{SAR}-{SLAM}: Self-Attentive Rendering-based {SLAM} with Neural Point Cloud Encoding},
author={Xudong Lv and Zhiwei He and Yuxiang Yang and Jiahao Nie and Jing Zhang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=VEGCPbt8H9}
}
Neural implicit representations have recently revolutionized simultaneous localization and mapping (SLAM), giving rise to a groundbreaking paradigm known as NeRF-based SLAM. However, existing methods often fall short in accurately estimating poses and reconstructing scenes. This limitation largely stems from their reliance on volume rendering techniques, which oversimplify the modeling process. In this paper, we introduce a novel neural implicit SLAM system designed to address these shortcomings. Our approach reconstructs Neural Radiance Fields (NeRFs) using a self-attentive architecture and represents scenes through neural point cloud encoding. Unlike previous NeRF-based SLAM methods, which depend on traditional volume rendering equations for scene representation and view synthesis, our method employs a self-attentive rendering framework with the Transformer architecture during mapping and tracking stages. To enable incremental mapping, we anchor scene features within a neural point cloud, striking a balance between estimation accuracy and computational cost. Experimental results across three challenging datasets demonstrate the superior performance and robustness of our proposed approach compared to recent NeRF-based SLAM systems. The code will be released.
SAR-SLAM: Self-Attentive Rendering-based SLAM with Neural Point Cloud Encoding
Xudong Lv Zhiwei He Yuxiang Yang Jiahao Nie Jing Zhang