NPLMV-PS: Neural Point-Light Multi-View Photometric Stereo (2405.12057v3)

Abstract: In this work we present a novel multi-view photometric stereo (MVPS) method. Like many works in 3D reconstruction we are leveraging neural shape representations and learnt renderers. However, our work differs from the state-of-the-art multi-view PS methods such as PS-NeRF or Supernormal in that we explicitly leverage per-pixel intensity renderings rather than relying mainly on estimated normals. We model point light attenuation and explicitly raytrace cast shadows in order to best approximate the incoming radiance for each point. The estimated incoming radiance is used as input to a fully neural material renderer that uses minimal prior assumptions and it is jointly optimised with the surface. Estimated normals and segmentation maps are also incorporated in order to maximise the surface accuracy. Our method is among the first (along with Supernormal) to outperform the classical MVPS approach proposed by the DiLiGenT-MV benchmark and achieves average 0.2mm Chamfer distance for objects imaged at approx 1.5m distance away with approximate 400x400 resolution. Moreover, our method shows high robustness to the sparse MVPS setup (6 views, 6 lights) greatly outperforming the SOTA competitor (0.38mm vs 0.61mm), illustrating the importance of neural rendering in multi-view photometric stereo.

• The paper presents NPLMV-PS, a method that directly leverages per-pixel intensities and a neural material renderer for precise 3D surface reconstruction.
• It achieves strong numerical performance on the DiLiGenT-MV benchmark, with average Chamfer distances of 0.2mm in ideal and 0.27mm in low-light conditions.
• The approach holds promise for advanced applications in AR/VR, robotics, and manufacturing by enhancing 3D object modeling and robustness.

Neural Point-Light Multi-View Photometric Stereo

Overview

This paper introduces NPLMV-PS, a new method for 3D reconstruction using multi-view photometric stereo (PS). Unlike previous approaches that rely heavily on estimated normals, this method leverages per-pixel intensity renderings. It’s a significant step forward in accurately modeling surface details, especially in challenging lighting conditions.

Key Differences from Existing Methods

While current leading methods like PS-NeRF and SuperNormal achieve impressive results, NPLMV-PS focuses on a few critical improvements:

1. Direct use of per-pixel intensities rather than only relying on estimated normals.
2. Incorporation of point light attenuation and raytracing of cast shadows, which provides a more accurate representation of incoming radiance.
3. A neural material renderer that works with minimal assumptions and is jointly optimized with the surface for higher accuracy.

Strong Numerical Results

One of the standout features of NPLMV-PS is its performance on the DiLiGenT-MV benchmark. This traditional benchmark involves objects imaged from different views under variable lighting:

• Average Chamfer distance: 0.2mm for objects at approximately 1.5m distance, with a resolution of about 400x400 pixels.
• Robustness in low-light scenarios: When fewer lights are available, and pixel rendering is used instead of estimated normals, the method still achieves a Chamfer distance of 0.27mm.

These numbers are particularly compelling because they highlight the method’s ability to maintain high accuracy in both ideal and less-than-ideal conditions.

Implications and Future Directions

Practical Implications

The ability to accurately reconstruct 3D surfaces from multi-view images has several practical applications:

• 3D object reconstruction for AR/VR, gaming, and cinematic effects.
• Quality control in manufacturing where precise 3D models can help in the verification and inspection processes.
• Robot interaction and navigation, where understanding object shapes and surfaces accurately can enhance performance.

Theoretical Implications

NPLMV-PS offers a new perspective on integrating classic computer vision techniques with neural networks. It shows that combining per-pixel intensity information with neural rendering can outperform methods relying heavily on normal maps.

Future Developments

Several intriguing avenues for future work stem from this research:

1. More complex materials and lighting scenarios: Extending the robustness and accuracy of this method to handle more complex BRDFs (Bidirectional Reflectance Distribution Functions) and varied lighting conditions.
2. Optimized neural architectures: Developing more efficient neural networks for real-time applications.
3. Integration with other 3D reconstruction tasks: Combining NPLMV-PS with other forms of 3D reconstruction such as lidar or depth sensing technologies to enhance accuracy and robustness in more diverse environments.

Conclusion

NPLMV-PS stands out as a robust method for 3D reconstruction using neural point-light multi-view photometric stereo. Its approach of leveraging per-pixel intensities, modeling point light attenuation, and using a neural material renderer sets a new standard in the field. The method not only provides highly accurate results but also shows promise for future advancements in both theoretical and practical aspects of AI-driven 3D reconstruction. So, if you're diving into multi-view PS, keep an eye on NPLMV-PS—it's a noteworthy development in the ongoing evolution of neural rendering techniques.