MicroSDF: Microfacet-Driven Hybrid Neural SDFs for Mixed-Reflectance Surface Reconstruction
To bridge this gap, we propose MicroSDF, a novel neural implicit framework that facilitates geometry and reflectance modeling through microfacet theory.
ProofPaper ↗
Key points
- Accurate 3D reconstruction in real-world environments remains a significant challenge due to the coexistence of reflective and non-reflective surfaces, which pose distinct modeling demands.
- Existing methods often treat these surface types separately, limiting their generalizability and physical plausibility.
- Combined with a tailored multi-stage optimization scheme, the proposed MicroSDF achieves robust and high-fidelity reconstruction across reflective and non-reflective surfaces.
- Extensive experiments on DTU, Shiny Blender, Ref-NeRF, and DeepVoxels datasets demonstrate state-of-the-art performance, establishing a new direction for physically grounded neural reconstruction.
Sources (1)
- [1]MicroSDF: Microfacet-Driven Hybrid Neural SDFs for Mixed-Reflectance Surface ReconstructionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 07:05 AM
To bridge this gap, we propose MicroSDF, a novel neural implicit framework that facilitates geometry and reflectance modeling through microfacet theory.
Accurate 3D reconstruction in real-world environments remains a significant challenge due to the coexistence of reflective and non-reflective surfaces, which pose distinct modeling demands.
Extractive summary: sentences quoted from the sources.