Unveiling the Ambiguity in Neural Inverse Rendering: A Parameter Compensation Analysis
arxiv(2024)
摘要
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.
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