Multi-view Reconstruction via SfM-guided Monocular Depth Estimation
The paper introduces "Murre," a novel framework aimed at enhancing multi-view 3D reconstruction by leveraging SfM-guided monocular depth estimation. This approach departs from traditional multi-view stereo (MVS) methods that suffer from high memory demands and limited performance in sparse view scenarios. By integrating Structure from Motion (SfM) priors into diffusion-based depth estimation, Murre addresses these two critical issues while maintaining high reconstruction quality and generalization capability.
Methodology and Contributions
The proposed method operates in a multi-phase pipeline that integrates SfM and diffusion-based monocular depth estimation for reconstructing 3D scenes. Initially, the method extracts sparse SfM point clouds from the input images, encapsulating the global scene structure. These point clouds guide the conditional diffusion model to predict multi-view consistent depth maps by bypassing the conventional multi-view matching process, traditionally used in MVS methods.
The notable design innovation presented in Murre is leveraging SfM point clouds as intermediate explicit representations, which effectively integrate multi-view information into the depth estimation task. This integration is achieved by converting the SfM point cloud into a sparse depth map, which is then densified and used to condition the monocular depth estimation. The method's reliance on this novel approach results in a model that aligns depth estimation with robust global scale accuracy and consistency.
Experimentally, Murre significantly outperforms state-of-the-art MVS and implicit neural reconstruction models across diverse datasets, including DTU, ScanNet, Replica, Waymo, and UrbanScene3D, establishing its effectiveness across various real-world scenarios. The model achieves superior performance in complex environments and demonstrates resilience to low-texture regions and sparse viewpoints.
Implications and Future Prospects
This research illustrates remarkable progress in resolving inherent challenges in image-based 3D reconstruction, such as memory inefficiency and scale ambiguity in depth estimation. By presenting a pipeline that combines diffusion models fine-tuned on synthetic data with established SfM techniques, this work opens new avenues for efficient, scalable, and robust 3D scene reconstructions.
From a practical standpoint, the integration of diffusion models with SfM broadens the application potential — extending from VR and AR fields to enhancing autonomous systems — where dense, accurate scene reconstructions from limited data are crucial. The model's ability to function effectively with minimal training data adds to its appeal, suggesting pathways for developing models that can generalize across vastly different environments.
The paper hints at future exploration in areas with extremely sparse view setups where current methods might still falter. Additionally, extending the model's capacity to handle dynamic elements in scenes could be essential for comprehensive real-time applications. As the landscape of large-scale synthetic data and foundational models continues to grow, the synthesis of such techniques with data-centric paradigms will likely propel further advancements in 3D computer vision.
Overall, Murre represents a significant methodological stride in multi-view 3D reconstruction, setting a precedent for combining robust traditional techniques with innovative deep learning models to overcome existing limitations in scene reconstruction quality and generalization.