Unveiling the Ambiguity in Neural Inverse Rendering: A Parameter Compensation Analysis

Georgios Kouros, Minye Wu, Sushruth Nagesh, Xianling Zhang, Tinne Tuytelaars; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 2832-2841

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.