  This work addresses the challenge of high-quality surface normal estimation
from monocular colored inputs (i.e., images and videos), a field which has
recently been revolutionized by repurposing diffusion priors. However, previous
attempts still struggle with stochastic inference, conflicting with the
deterministic nature of the Image2Normal task, and costly ensembling step,
which slows down the estimation process. Our method, StableNormal, mitigates
the stochasticity of the diffusion process by reducing inference variance, thus
producing "Stable-and-Sharp" normal estimates without any additional ensembling
process. StableNormal works robustly under challenging imaging conditions, such
as extreme lighting, blurring, and low quality. It is also robust against
transparent and reflective surfaces, as well as cluttered scenes with numerous
objects. Specifically, StableNormal employs a coarse-to-fine strategy, which
starts with a one-step normal estimator (YOSO) to derive an initial normal
guess, that is relatively coarse but reliable, then followed by a
semantic-guided refinement process (SG-DRN) that refines the normals to recover
geometric details. The effectiveness of StableNormal is demonstrated through
competitive performance in standard datasets such as DIODE-indoor, iBims,
ScannetV2 and NYUv2, and also in various downstream tasks, such as surface
reconstruction and normal enhancement. These results evidence that StableNormal
retains both the "stability" and "sharpness" for accurate normal estimation.
StableNormal represents a baby attempt to repurpose diffusion priors for
deterministic estimation. To democratize this, code and models have been
publicly available in hf.co/Stable-X
