NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising

A novel dense SLAM system is proposed with hierarchical implicit scene representation. This system is scalable, predictive, and robust to complex indoor scenes. It is an end-to-end, incrementally optimizable method for tracking and mapping. It offers the capability of generating photo-realistic novel views and producing accurate 3D meshes. We present a supplementary material and a video of our paper.