Unveiling the Ambiguity in Neural Inverse Rendering: A Parameter Compensation Analysis
This work addresses the ill-posed nature of inverse rendering for computer vision researchers, providing insights into ambiguity but is incremental as it builds on existing methods without major breakthroughs.
The paper tackles the problem of ambiguity in neural inverse rendering, where scene properties are estimated from multiview images, by analyzing parameter compensation using a disentangled Neural Microfacet Fields method, revealing that adjustments in one property can compensate for perturbations in another.
Inverse rendering aims to reconstruct the scene properties of objects solely from multiview images. However, it is an ill-posed problem prone to producing ambiguous estimations deviating from physically accurate representations. In this paper, we utilize Neural Microfacet Fields (NMF), a state-of-the-art neural inverse rendering method to illustrate the inherent ambiguity. We propose an evaluation framework to assess the degree of compensation or interaction between the estimated scene properties, aiming to explore the mechanisms behind this ill-posed problem and potential mitigation strategies. Specifically, we introduce artificial perturbations to one scene property and examine how adjusting another property can compensate for these perturbations. To facilitate such experiments, we introduce a disentangled NMF where material properties are independent. The experimental findings underscore the intrinsic ambiguity present in neural inverse rendering and highlight the importance of providing additional guidance through geometry, material, and illumination priors.