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

arXiv:2403.20034v1 Announce Type: new
Abstract: In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes implicit neural representation to encode 3D scenes. This extension of NeRF to SLAM has shown promising results. However, the depth images obtained from consumer-grade RGB-D sensors are often sparse and noisy, which poses significant challenges for 3D reconstruction and affects the accuracy of …

3d reconstruction 3d scenes abstract application arxiv cs.cv cs.ro denoising development encode extension feature fields mapping nerf neural radiance fields representation rgb-d slam systems tracking type