Consider the information of the image itself

[pylittlebrat]

Your normal-based approach shows remarkable potential in geometric reconstruction, yet its heavy reliance on normal maps introduces inherent vulnerabilities – particularly when handling textureless regions, specular artifacts, or ambiguous lighting conditions. A compelling enhancement would be developing a physics-aware fusion mechanism that treats the original RGB image as complementary evidence.

By establishing soft constraints between geometric hypotheses (from normals) and photometric patterns (like albedo consistency, shadow coherence, and edge/texture alignment), the system could self-correct normal estimation errors while preserving valid geometric details. This might be achieved through learnable cross-modal attention layers that dynamically weight geometric vs. appearance cues based on local feature reliability, creating a more robust and self-stabilizing reconstruction pipeline without requiring major architectural overhauls.