PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo (2207.11406v2)

Abstract: Traditional multi-view photometric stereo (MVPS) methods are often composed of multiple disjoint stages, resulting in noticeable accumulated errors. In this paper, we present a neural inverse rendering method for MVPS based on implicit representation. Given multi-view images of a non-Lambertian object illuminated by multiple unknown directional lights, our method jointly estimates the geometry, materials, and lights. Our method first employs multi-light images to estimate per-view surface normal maps, which are used to regularize the normals derived from the neural radiance field. It then jointly optimizes the surface normals, spatially-varying BRDFs, and lights based on a shadow-aware differentiable rendering layer. After optimization, the reconstructed object can be used for novel-view rendering, relighting, and material editing. Experiments on both synthetic and real datasets demonstrate that our method achieves far more accurate shape reconstruction than existing MVPS and neural rendering methods. Our code and model can be found at https://ywq.github.io/psnerf.

• The paper introduces a joint optimization framework that refines shape, BRDFs, and lighting through neural inverse rendering to reduce cumulative errors.
• It employs surface normal regularization from multi-light images to enhance reconstruction accuracy under sparse view conditions.
• Empirical results show state-of-the-art performance in fine detail recovery, paving the way for advanced 3D applications in various fields.

An Overview of PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo

The paper "PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo" introduces a novel approach aimed at enhancing the precision of 3D shape reconstruction through a methodology that integrates insights from neural rendering and photometric stereo techniques. Traditional multi-view photometric stereo (MVPS) approaches often struggle with multiple disjoint stages, leading to a significant accumulation of errors. Thus, this paper proposes a method that systematically addresses these challenges and demonstrates noteworthy improvements in shape reconstruction accuracy.

Key Contributions and Methodology

PS-NeRF extends the capabilities of neural rendering by jointly optimizing crucial elements such as geometry, materials, and lighting conditions. The authors employ implicit representation techniques to develop a more cohesive framework for MVPS. The core contributions of this method include:

• Joint Optimization Framework: The technique involves using neural inverse rendering to simultaneously optimize shape, bidirectional reflectance distribution functions (BRDFs), and lighting conditions through a shadow-aware differentiable rendering layer.
• Surface Normal Regularization: The method introduces a mechanism to regularize surface normals derived from the radiance field using normals estimated from multi-light images, thereby significantly bolstering surface reconstruction, especially under sparse view conditions.
• State-of-the-art Performance: Empirical results demonstrate that the PS-NeRF technique outperforms existing MVPS and neural rendering methods in terms of both quantitative metrics and visual quality, particularly in reconstructing fine surface details.

Methodological Insights

The paper elucidates a two-stage methodological process:

1. Initial Shape Modeling: Utilizes a neural radiance field, with surface normal regularizations inferred from multi-light images, to obtain an initial estimation of the object surface.
2. Joint Optimization via Inverse Rendering: Building upon the initial shape prior, this stage jointly optimizes surface normals, BRDFs, and light configurations using a shadow-aware rendering framework. This step is pivotal for disentangling geometry from lighting and reflectance properties, culminating in rendered images that remain faithful to the input under varying light conditions.

Implications for Future Research and Applications

The implications of this research extend to potential advancements in computer vision tasks that require precise 3D surface reconstruction, such as those in augmented reality, video game development, and autonomous navigation. The joint optimization strategy, particularly in dealing with uncalibrated lighting, opens avenues for improving photorealistic rendering and coherent scene understanding under practical scenarios.

Moreover, PS-NeRF's framework could be adapted or extended to accommodate non-static and dynamic lighting conditions, as well as varied surface reflectance types, thereby broadening its applicability to more complex real-world settings. Further research might explore the integration of PS-NeRF with real-time processing architectures or its adaptation into end-to-end systems for automated 3D content generation and manipulation.

In summary, the PS-NeRF model demonstrates that leveraging neural inverse rendering in conjunction with multi-view photometric stereo can substantially improve 3D shape reconstruction accuracy. The method stands as a promising advance, charting a path for future inquiry and innovation in the domain of neural rendering and photometric stereo systems.