Research Paper#3D Reconstruction, Diffusion Models, Computer Vision🔬 ResearchAnalyzed: Jan 3, 2026 06:32
GaMO: Geometry-aware Diffusion for Sparse-View 3D Reconstruction
Published:Dec 31, 2025 18:59
•1 min read
•ArXiv
Analysis
This paper introduces GaMO, a novel framework for 3D reconstruction from sparse views. It addresses limitations of existing diffusion-based methods by focusing on multi-view outpainting, expanding the field of view rather than generating new viewpoints. This approach preserves geometric consistency and provides broader scene coverage, leading to improved reconstruction quality and significant speed improvements. The zero-shot nature of the method is also noteworthy.
Key Takeaways
- •GaMO addresses limitations of existing diffusion-based 3D reconstruction methods.
- •It uses multi-view outpainting to expand the field of view, preserving geometric consistency.
- •GaMO achieves state-of-the-art reconstruction quality with significant speed improvements.
- •The method operates in a zero-shot manner, without requiring training.
Reference
“GaMO expands the field of view from existing camera poses, which inherently preserves geometric consistency while providing broader scene coverage.”