Monocular Online Reconstruction with Enhanced Detail Preservation
This improves photorealistic 3D mapping from monocular video for applications like robotics or AR/VR, though it appears incremental as it builds on existing Gaussian-based methods.
The paper tackles monocular online 3D reconstruction by proposing a Gaussian-based framework that addresses Gaussian distribution without depth maps and ensures multi-scale consistency, achieving superior reconstruction quality compared to state-of-the-art RGB-only and RGB-D methods with high computational efficiency.
We propose an online 3D Gaussian-based dense mapping framework for photorealistic details reconstruction from a monocular image stream. Our approach addresses two key challenges in monocular online reconstruction: distributing Gaussians without relying on depth maps and ensuring both local and global consistency in the reconstructed maps. To achieve this, we introduce two key modules: the Hierarchical Gaussian Management Module for effective Gaussian distribution and the Global Consistency Optimization Module for maintaining alignment and coherence at all scales. In addition, we present the Multi-level Occupancy Hash Voxels (MOHV), a structure that regularizes Gaussians for capturing details across multiple levels of granularity. MOHV ensures accurate reconstruction of both fine and coarse geometries and textures, preserving intricate details while maintaining overall structural integrity. Compared to state-of-the-art RGB-only and even RGB-D methods, our framework achieves superior reconstruction quality with high computational efficiency. Moreover, it integrates seamlessly with various tracking systems, ensuring generality and scalability.