PISR: Polarimetric Neural Implicit Surface Reconstruction for Textureless and Specular Objects (2409.14331v1)

Abstract: Neural implicit surface reconstruction has achieved remarkable progress recently. Despite resorting to complex radiance modeling, state-of-the-art methods still struggle with textureless and specular surfaces. Different from RGB images, polarization images can provide direct constraints on the azimuth angles of the surface normals. In this paper, we present PISR, a novel method that utilizes a geometrically accurate polarimetric loss to refine shape independently of appearance. In addition, PISR smooths surface normals in image space to eliminate severe shape distortions and leverages the hash-grid-based neural signed distance function to accelerate the reconstruction. Experimental results demonstrate that PISR achieves higher accuracy and robustness, with an L1 Chamfer distance of 0.5 mm and an F-score of 99.5% at 1 mm, while converging 4~30 times faster than previous polarimetric surface reconstruction methods.

• The paper introduces a polarimetric loss based on perspective constraints to resolve shape-radiance ambiguity in reconstructing textureless and specular objects.
• It employs a multi-resolution hash grid neural signed distance function that accelerates 3D reconstruction by up to 30×.
• Experimental results show significant improvements in accuracy, achieving an L1 Chamfer distance of 0.5 mm and an F-score of 99.5%.

Polarimetric Neural Implicit Surface Reconstruction for Textureless and Specular Objects

The paper "PISR: Polarimetric Neural Implicit Surface Reconstruction for Textureless and Specular Objects" introduces a novel method leveraging polarization cues combined with neural implicit surfaces to achieve highly accurate 3D reconstructions. Authored by Guangcheng Chen, Yicheng He, Li He, and Hong Zhang, it tackles the fundamental problem of surface reconstruction specifically for textureless and specular objects, which pose significant challenges for state-of-the-art methods.

Summary

PISR addresses the shape-radiance ambiguity inherent in traditional RGB image-based reconstruction methods by integrating polarimetric constraints. Unlike conventional approaches that rely on complex radiance models, PISR employs a geometrically accurate polarimetric loss function to refine shapes independently of appearance. This is particularly critical for objects like ceramics and plastics, which exhibit textureless and specular properties.

The authors propose a polarimetric loss based on the perspective polarimetric constraint, which considers the camera ray direction to model the perspective effect of the lens more accurately. This improves upon the traditional orthographic constraint by reducing shape distortions due to perspective effects, resulting in a significant reduction in the L1 Chamfer distance.

Methodology

The method combines multiple innovative components:

1. Polarimetric Loss: The perspective polarimetric constraint is used to derive a novel polarimetric loss that effectively addresses the $\pi/2$-ambiguity by adaptively adjusting according to the degree of polarization.
2. Normal Regularization: The method incorporates a normal regularization technique by smoothing surface normals in the image space, thereby mitigating the artifacts introduced by the discrete nature of the hash grid representation.
3. Multi-resolution Hash Grid: PISR uses a hash-grid-based neural signed distance function (SDF) to represent the object, which accelerates the reconstruction process by $4\sim30\times$ compared to previous methods.

Results

Experimental evaluation demonstrates that PISR outperforms existing methods both in terms of accuracy and efficiency. The experiments on a real-world dataset featuring textureless and specular objects show that PISR achieves an L1 Chamfer distance of $0.5 \, \text{mm}$ and an F-score of $99.5\%$ at $1 \, \text{mm}$. This represents a considerable improvement over previous methods like PMVIR and PANDORA.

Implications

The practical implications of PISR are significant for fields such as computer graphics and robotics, where accurate 3D models of objects are essential. The method's robustness and speed make it suitable for applications involving real-time 3D scanning and reconstruction. On a theoretical level, the use of the perspective polarimetric constraint represents an advancement in how polarization information can be exploited for 3D reconstruction.

Future Directions

Potential future developments include improving the robustness of the polarimetric loss function to handle noisy angle of polarization (AoP) maps. This would extend PISR’s applicability to rough, well-textured, or mirror-like objects. Additionally, integrating shape constraints from normal and depth estimation models could enable the method to perform accurately with sparser input views, thereby broadening its practical utility.

Conclusion

PISR represents a sophisticated fusion of polarization imaging and neural implicit surfaces, delivering high-accuracy 3D reconstructions for challenging textureless and specular objects. The combination of polarimetric constraints with a hash-grid-based neural SDF sets a new standard in the field, offering both theoretical and practical advancements in computer vision and related disciplines. The authors have provided substantial empirical evidence to support their claims, paving the way for further research and development in this domain.