Modeling Indirect Illumination for Inverse Rendering (2204.06837v1)

Abstract: Recent advances in implicit neural representations and differentiable rendering make it possible to simultaneously recover the geometry and materials of an object from multi-view RGB images captured under unknown static illumination. Despite the promising results achieved, indirect illumination is rarely modeled in previous methods, as it requires expensive recursive path tracing which makes the inverse rendering computationally intractable. In this paper, we propose a novel approach to efficiently recovering spatially-varying indirect illumination. The key insight is that indirect illumination can be conveniently derived from the neural radiance field learned from input images instead of being estimated jointly with direct illumination and materials. By properly modeling the indirect illumination and visibility of direct illumination, interreflection- and shadow-free albedo can be recovered. The experiments on both synthetic and real data demonstrate the superior performance of our approach compared to previous work and its capability to synthesize realistic renderings under novel viewpoints and illumination. Our code and data are available at https://zju3dv.github.io/invrender/.

• The paper introduces an MLP-based indirect illumination model derived from neural radiance fields, significantly reducing computational costs.
• The method sequentially learns geometry, radiance fields, and optimizes SVBRDF and direct illumination to improve rendering quality.
• Experiments demonstrate enhanced inverse rendering with improved shadow-free albedo and robust relighting under novel viewpoints.

Modeling Indirect Illumination for Inverse Rendering

The paper "Modeling Indirect Illumination for Inverse Rendering" by Yuanqing Zhang et al. addresses a significant challenge in the field of computer vision and graphics: recovering the geometry, materials, and lighting conditions of a 3D scene from images, specifically under the constraints of unknown static illumination. The research introduces an innovative methodology for tackling the issue of indirect illumination in inverse rendering by utilizing neural radiance fields.

Overview of Contributions

The research distinguishes itself by avoiding the high computational costs of recursive path tracing typically needed to model indirect illumination. Instead, the authors propose the derivation of indirect illumination from the neural radiance field constructed from input images. This approach not only enhances efficiency but significantly improves the quality of inverse rendering.

Methodology

The core contribution is the indirect illumination model depicted as a multilayer perceptron (MLP), which maps 3D surface points to their corresponding indirect incoming illumination. This is coupled with a sparse latent space for spatially varying bidirectional reflectance distribution function (SVBRDF), allowing the model to leverage material priors effectively. The process unfolds in three stages:

1. Geometric and Radiance Field Learning: Utilizing methods such as IDR, the geometry and outgoing radiance field are learned from input images.
2. Indirect Illumination Derivation: Indirect illumination is trained using the known outgoing radiance field, providing a rich set of data points without resorting to costly recursive tracing.
3. Rendering Optimization: Models for SVBRDF and direct illumination are refined by minimizing rendering discrepancies with observed images.

Experimental Insights

Quantitative and qualitative assessments demonstrate the superiority of this method over prior approaches like NeRFactor and PhySG. It significantly enhances the recovery of shadow- and interreflection-free albedo and offers robust capabilities for synthesizing renderings under novel viewpoints and lighting conditions. For real-world captures, the method convincingly decomposes observed images into their underlying factors, enabling subsequent relighting to produce realistic results.

Implications and Potential Directions

This work has meaningful implications for the development of more efficient and accurate inverse rendering techniques. By focusing on pre-learned radiance fields, it provides a pathway to handle complex lighting with lower computational demands. Furthermore, the treatment of multiple types of material optimally aligns with real-world scenarios, especially in augmented and virtual reality applications.

Future developments could include refining BRDF assumptions and incorporating dynamic lighting conditions, broadening the applicability of the technique to even more diverse and unpredictable settings. Moreover, improvements in accuracy and detail of geometric representation could further enhance the method's performance and reliability.

In conclusion, the authors present a well-founded approach to addressing the computational barrier in indirect illumination modeling within inverse rendering. Their methodology is a vital contribution to advancing both the practical implementation and theoretical understanding of rendering in computer vision.