Region sampling NeRF-SLAM based on Kolmogorov-Arnold network.

ABSTRACT: Currently, NeRF-based SLAM is rapidly developing in reconstructing and bitwise estimating indoor scenes. Compared with traditional SLAM, the advantage of the NeRF-based approach is that the error returns to the pixel itself, the optimization process is WYSIWYG, and it can also be differentiated for map representation. Still, it is limited by its MLP-based implicit representation to scale to larger and more complex environments. Inspired by the quadtree in ORB-SLAM2 and the recently proposed Kolmogorov-Arnold network, our approach replaces the MLP with a KAN network based on Gaussian functions, combines quadtree-based regional pixel sampling and random sampling, delineates the scene by voxels, and supports dynamic scaling to realize a high-fidelity reconstruction of large scenes for a SLAM

SUBMITTER: Li Z 

PROVIDER: S-EPMC12111630 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature