# MISO: Multiresolution Submap Optimization for Efficient Globally Consistent Neural Implicit Reconstruction

arXiv: https://arxiv.org/abs/2504.19104 · HTML: https://mlsanity.com/paper/2504.19104/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2025.

## Built on by (most cited first, 3 of 3)

- [SERF: Spatiotemporal Environment and Robot Feature Map for Long-Horizon Mobile Manipulation](https://mlsanity.com/paper/2606.12956/) (2026)
- [From Distances to Trajectories: Real-Time Signed Distance Function Mapping and Distance-Accelerated Motion Planning for UAVs](https://mlsanity.com/paper/2607.19306/) (2026)
- [HIGS: Hierarchical Implicit Grids for Joint Geometric and Semantic Scene Understanding](https://mlsanity.com/paper/2609.38620/) (2026)

## Abstract (arXiv)

Neural implicit representations have had a significant impact on simultaneous localization and mapping (SLAM) by enabling robots to build continuous, differentiable, and high-fidelity 3D maps from sensor data. However, as the scale and complexity of the environment increase, neural SLAM approaches face renewed challenges in the back-end optimization process to keep up with runtime requirements and maintain global consistency. We introduce MISO, a hierarchical optimization approach that leverages multiresolution submaps to achieve efficient and scalable neural implicit reconstruction. For local SLAM within each submap, we develop a hierarchical optimization scheme with learned initialization that substantially reduces the time needed to optimize the implicit submap features. To correct estimation drift globally, we develop a hierarchical method to align and fuse the multiresolution submaps, leading to substantial acceleration by avoiding the need to decode the full scene geometry. MISO significantly improves computational efficiency and estimation accuracy of neural signed distance function (SDF) SLAM on large-scale real-world benchmarks.