NeuV-SLAM: Fast Neural Multiresolution Voxel Optimization for RGBD Dense SLAM

TL;DR

NeuV-SLAM introduces a fast neural multiresolution voxel-based SLAM system that achieves rapid scene convergence and accurate incremental mapping using novel implicit representations and efficient voxel management, validated on standard datasets.

Contribution

The paper presents a novel neural multiresolution voxel optimization framework with a new implicit SDF representation and hash-based voxel management for fast, accurate RGBD dense SLAM.

Findings

Achieves rapid scene convergence and robust incremental scene reconstruction.

Demonstrates superior tracking accuracy and scene rendering quality.

Validated on Replica and ScanNet datasets with strong empirical results.

Abstract

We introduce NeuV-SLAM, a novel dense simultaneous localization and mapping pipeline based on neural multiresolution voxels, characterized by ultra-fast convergence and incremental expansion capabilities. This pipeline utilizes RGBD images as input to construct multiresolution neural voxels, achieving rapid convergence while maintaining robust incremental scene reconstruction and camera tracking. Central to our methodology is to propose a novel implicit representation, termed VDF that combines the implementation of neural signed distance field (SDF) voxels with an SDF activation strategy. This approach entails the direct optimization of color features and SDF values anchored within the voxels, substantially enhancing the rate of scene convergence. To ensure the acquisition of clear edge delineation, SDF activation is designed, which maintains exemplary scene representation fidelity even under constraints of voxel resolution. Furthermore, in pursuit of advancing rapid incremental expansion with low computational overhead, we developed hashMV, a novel hash-based multiresolution voxel management structure. This architecture is complemented by a strategically designed voxel generation technique that synergizes with a two-dimensional scene prior. Our empirical evaluations, conducted on the Replica and ScanNet Datasets, substantiate NeuV-SLAM's exceptional efficacy in terms of convergence speed, tracking accuracy, scene reconstruction, and rendering quality.