Multi-times Monte Carlo Rendering for Inter-reflection Reconstruction

TL;DR

This paper introduces Ref-MC2, a novel inverse rendering approach that employs multi-time Monte Carlo sampling and a reflection-aware surface model to accurately reconstruct reflective surfaces with inter-reflections, outperforming existing methods.

Contribution

The paper proposes a multi-time Monte Carlo sampling technique with a specularity-adaptive strategy and a reflection-aware surface model for improved inter-reflection reconstruction in inverse rendering.

Findings

Outperforms existing inverse rendering methods on complex scenes.

Effectively handles inter-reflections among multiple objects.

Enables downstream applications like relighting and material editing.

Abstract

Inverse rendering methods have achieved remarkable performance in reconstructing high-fidelity 3D objects with disentangled geometries, materials, and environmental light. However, they still face huge challenges in reflective surface reconstruction. Although recent methods model the light trace to learn specularity, the ignorance of indirect illumination makes it hard to handle inter-reflections among multiple smooth objects. In this work, we propose Ref-MC2 that introduces the multi-time Monte Carlo sampling which comprehensively computes the environmental illumination and meanwhile considers the reflective light from object surfaces. To address the computation challenge as the times of Monte Carlo sampling grow, we propose a specularity-adaptive sampling strategy, significantly reducing the computational complexity. Besides the computational resource, higher geometry accuracy is also required because geometric errors accumulate multiple times. Therefore, we further introduce a reflection-aware surface model to initialize the geometry and refine it during inverse rendering. We construct a challenging dataset containing scenes with multiple objects and inter-reflections. Experiments show that our method outperforms other inverse rendering methods on various object groups. We also show downstream applications, e.g., relighting and material editing, to illustrate the disentanglement ability of our method.