Practical Solutions for Sparse-view 3D Reconstruction with LM-Gaussian
Overview
LM-Gaussian leverages large model priors to enhance 3D scene reconstruction from limited images, addressing challenges in sparse-view scenarios. The method significantly reduces data acquisition requirements while maintaining high-quality results in 360-degree scenes.
Key Features
- Robust initialization module for camera pose recovery and point cloud generation
- Multi-modal regularization techniques for smoother surfaces and reduced artifacts
- Iterative diffusion refinement for enhancing image quality and high-frequency details
Value
LM-Gaussian outperforms baseline methods, achieving high-quality reconstructions from just 16 images. It excels in sparse-view scenarios, preserving structures and details better than competitors while reducing data acquisition requirements.
Future Potential
Future work aims to expand LM-Gaussian’s applicability to dynamic modeling and further improve its effectiveness in various 3D reconstruction scenarios.
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