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

TL;DR

This paper investigates the inherent ambiguity in neural inverse rendering by using Neural Microfacet Fields (NMF) as a testbed. It proposes a compensation-analysis framework that perturbs one scene property while independently fine-tuning others, aided by a disentangled NMF architecture to isolate material parameters. The experiments on shiny-blender scenes under constant illumination show that the method can compensate for suboptimal albedo $\rho$ and $F_0$, but has difficulty correcting errors in roughness $\alpha$, density $\sigma$, or illumination, highlighting fundamental ambiguity. The authors advocate geometry, material, and illumination priors—such as pre-trained BRDFs and generative illumination priors—to constrain the search space and improve relighting consistency.

Abstract

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.

Figures (6)

• Figure 1: Demonstration of the inherent ambiguity in the task of neural inverse rendering. Training an inverse rendering algorithm like NMF mai2023nmf on the same object under different illuminations yields varying material properties that subsequently impact relighting tasks.
• Figure 2: An example of suboptimal estimation of material properties and illumination. The model underestimates the roughness of the ball and overestimates the smoothness of the environment map, which results in sharper reflections when relighting the scene.
• Figure 3: Disentanglement of appearance features to enable independent fine-tuning of albedo $\mathit{\rho}$, roughness $\mathit{a}$, and $\mathit{F_0}$. The single appearance TensoRF is split into three distinct TensoRFs with one third of the original feature dimensionality. This ensures that fine-tuning the albedo, roughness, or $F_0$ will not impact the input feature space of the other two. The TensoRF visualization was adapted from the original paper Chen2022tensorf.
• Figure 4: Estimated scene properties of the car and helmet scenes from the shiny-blender dataset under different illuminations. Due to the underconstrained nature of the problem and NMF optimizing solely for reconstruction, it fails to learn consistent scene properties.
• Figure 5: Graphs demonstrating to what extent one scene property can compensate for suboptimal estimation of other properties. The vertical axis corresponds to the manipulated scene property while the horizontal axis corresponds to the fine-tuned property. The arrows $\uparrow, \downarrow$ on the manipulated property labels refer to overestimating or underestimating the property, respectively. A weighted average is used across the examined scenes (ball, car, helmet) of the shiny-blender dataset.