Summary of Stablenormal: Reducing Diffusion Variance For Stable and Sharp Normal, by Chongjie Ye et al.

StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal

by Chongjie Ye, Lingteng Qiu, Xiaodong Gu, Qi Zuo, Yushuang Wu, Zilong Dong, Liefeng Bo, Yuliang Xiu, Xiaoguang Han

First submitted to arxiv on: 24 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper addresses the challenge of estimating high-quality surface normals from monocular colored inputs, a field revolutionized by repurposing diffusion priors. However, previous methods struggle with stochastic inference, conflicting with the deterministic nature of the Image2Normal task and requiring costly ensembling steps. The proposed StableNormal method mitigates this stochasticity by reducing inference variance, producing “Stable-and-Sharp” normal estimates without additional ensembling. This method is robust under challenging imaging conditions, including extreme lighting, blurring, low quality, transparent surfaces, reflective surfaces, and cluttered scenes. It employs a coarse-to-fine strategy, starting with a one-step normal estimator (YOSO) to derive an initial guess, then refining the normals through semantic-guided refinement (SG-DRN). The paper demonstrates competitive performance on standard datasets like DIODE-indoor, iBims, ScannetV2, and NYUv2, as well as downstream tasks like surface reconstruction and normal enhancement. These results show that StableNormal retains both “stability” and “sharpness” for accurate normal estimation.
Low	GrooveSquid.com (original content)	Low Difficulty Summary StableNormal is a new method for estimating high-quality surface normals from images and videos. Right now, computers have trouble doing this because they make mistakes when trying to figure out what the surface looks like. The creators of StableNormal found a way to fix these mistakes by making their computer program more careful and precise. This means that it can work well even in situations where the lighting is bad or there are lots of distracting objects in the scene. It does this by using two steps: first, it makes a rough guess about what the surface looks like, and then it refines that guess to get a more accurate answer. The people who created StableNormal tested their program on many different types of images and videos, and it did well.