Spatial Coordinate Transformation for 3D Neural Implicit Mapping

Abstract

Implicit Neural Representation (INR)-based SLAM has a critical issue where all keyframes must be stored in memory for post-training whenever a remapping is needed due to the neural network's weights themselves representing the map. To address this, previous INR-based SLAM proposed methods to modify INR-based maps without changing the neural network's weights. However, these approaches suffer from low memory efficiency and increased space complexity. In this paper, we introduce a remapping method for INR-based maps that does not require post-traning the neural network's weights and needed low space cost. The problem of function modification, such as updating a map defined as a neural network function, can be viewed as transforming the function’s domain. Leveraging function domain transformation, we propose a method to update INR-based maps by identifying the transformation function between the post-optimization and pre-optimization domains. Additionally, to prevent cases where the transformation between the post-optimization and pre-optimization domains does not form a one-to-many relationship, we introduce a temporal domain and propose a method to find the spatial coordinate transformation function accordingly. Evaluations in INR-based techniques demonstrate that our proposed method effectively update to maps while requiring significantly less memory compared to existing remapping approaches.