# MonoPrior-Fusion: Monocular-Prior-Guided Multi-Frame Depth Estimation with Multi-Scale Geometric Fusion

## TL;DR

This paper introduces a new method for depth estimation in indoor environments using monocular priors and multi-scale fusion to improve 3D perception.

## Contribution

The novel framework, MonoPrior-Fusion, integrates monocular priors into multi-view matching and introduces a geometric consistency loss for better 3D coherence.

## Key findings

- MPF outperforms state-of-the-art methods in challenging indoor scenarios.
- The method improves 3D reconstruction accuracy and completeness in volumetric fusion pipelines.

## Abstract

Precise 3D perception is critical for indoor robotics, augmented reality, and autonomous navigation. However, existing multi-frame depth estimation methods often suffer from significant performance degradation in challenging indoor scenarios characterized by weak textures, non-Lambertian surfaces, and complex layouts. To address these limitations, we propose MonoPrior-Fusion (MPF), a novel framework that integrates pixel-wise monocular priors directly into the multi-view matching process. Specifically, MPF modulates cost-volume hypotheses to disambiguate matches and employs a hierarchical fusion architecture across multiple scales to propagate global and local geometric information. Additionally, a geometric consistency loss based on virtual planes is introduced to enhance global 3D coherence. Extensive experiments on ScanNetV2, 7Scenes, TUM RGB-D, and GMU Kitchens demonstrate that MPF achieves significant improvements over state-of-the-art multi-frame baselines and generalizes well across unseen domains. Furthermore, MPF yields more accurate and complete 3D reconstructions when integrated into a volumetric fusion pipeline, proving its effectiveness for dense mapping tasks. The source code will be made publicly available to support reproducibility and future research.