End-to-End RGB-D SLAM With Multi-MLPs Dense Neural Implicit Representations

An accurate and generalizable dense 3D reconstruction system has attracted much attention. However, existing 3D dense reconstruction systems are constrained by pre-training, and there is a need for enhanced reconstruction of texture and shape details. We propose an end-to-end 3D reconstruction system which achieves fine scene reconstruction without prior information by utilizing a neural implicit encoding. Our proposed system successfully achieves the goal through improved multi-MLP decoders (MLM) and an effective keyframe selection strategy. Experiments conducted on the commonly used Replica and TUM RGB-D datasets demonstrate that our approach can compete with widely adopted NeRF-based SLAM methods in terms of 3D reconstruction accuracy. Moreover, our approach shows a 40.8%(except Completion Ratio) improvement in accuracy compared to NICE-SLAM [14] and does not use prior information.

Automated summary

In this paper, we propose an end-to-end 3D reconstruction system that achieves fine scene reconstruction without prior information by utilizing a neural implicit encoding. Our approach, with improved decoders and a keyframe selection strategy, can compete with widely adopted NeRF-based SLAM methods in terms of 3D reconstruction accuracy.