VoxNeuS: Enhancing Voxel-Based Neural Surface Reconstruction via Gradient Interpolation (2406.07170v1)

Abstract: Neural Surface Reconstruction learns a Signed Distance Field~(SDF) to reconstruct the 3D model from multi-view images. Previous works adopt voxel-based explicit representation to improve efficiency. However, they ignored the gradient instability of interpolation in the voxel grid, leading to degradation on convergence and smoothness. Besides, previous works entangled the optimization of geometry and radiance, which leads to the deformation of geometry to explain radiance, causing artifacts when reconstructing textured planes. In this work, we reveal that the instability of gradient comes from its discontinuity during trilinear interpolation, and propose to use the interpolated gradient instead of the original analytical gradient to eliminate the discontinuity. Based on gradient interpolation, we propose VoxNeuS, a lightweight surface reconstruction method for computational and memory efficient neural surface reconstruction. Thanks to the explicit representation, the gradient of regularization terms, i.e. Eikonal and curvature loss, are directly solved, avoiding computation and memory-access overhead. Further, VoxNeuS adopts a geometry-radiance disentangled architecture to handle the geometry deformation from radiance optimization. The experimental results show that VoxNeuS achieves better reconstruction quality than previous works. The entire training process takes 15 minutes and less than 3 GB of memory on a single 2080ti GPU.

• The paper proposes a novel gradient interpolation technique that significantly refines voxel-based neural surface reconstruction.
• It integrates deep learning with voxel representations and gradient cues, outperforming traditional reconstruction methods.
• The method achieves superior accuracy in rendering complex surfaces, paving the way for real-time graphics and vision applications.

An Analysis of the ACM Consolidated LaTeX Article Template

The paper "The Name of the Title is Hope" provides an in-depth overview and practical guidance on utilizing the ACM's consolidated LaTeX article template, introduced in 2017. This template is designed to provide a consistent style across ACM publications, accommodating a range of article types from conference proceedings to journal publications. The adoption of this template streamlines the process of preparing manuscripts for submission by offering a unified format that incorporates both accessibility and metadata-extraction functionalities essential for future digital library endeavors.

Key Features and Utility

One of the salient features of this template is its flexibility. The core document class, acmart, can be adapted for various types of ACM publications by simply adjusting the template styles and parameters. The primary parameter, template style, dictates the formatting specifics based on the publication type or SIG group. For example:

• Journals: Default (acmsmall), ACM Large (acmlarge), ACM TOG (acmtog)
• Conference Proceedings: Default (acmconf), SIGCHI (sigchi), SIGPLAN (sigplan)

Additional parameters can further customize the presentation, such as anonymizing submissions for dual-anonymous review or producing author versions for personal dissemination.

Special Formatting Instructions

The paper emphasizes strict adherence to template modifications. Any unauthorized adjustments in margins, typeface sizes, or line spacing are prohibited, ensuring documents maintain the standardized format expected by ACM. This rigidity extends to the use of typefaces, mandating the Libertine typeface family without substitutions.

Sections, Citations, and Bibliographies

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Citations and bibliographies are managed through BibTeX, with a preference for detailed and complete references. Authors' names should be provided in full to ensure correct attribution and retrieval.

Accessibility and Metadata

Incorporating accessibility measures, the template supports descriptive text for figures, ensuring they are accessible to all users, including those relying on screen readers. The use of the ACM Computing Classification System (CCS) and user-defined keywords further aids in categorizing the research, improving discoverability.

Practical Implications and Future Directions

The implementation of this consolidated template offers practical benefits by reducing the complexity involved in formatting manuscripts and ensuring a consistent presentation across ACM's vast range of publications. This consistency not only simplifies the submission process for authors but also enhances the reader's experience by providing a predictable and professional layout.

Theoretically, the adoption of a unified template system may encourage other academic publishers to adopt similar strategies, promoting standardization across the scholarly publishing industry. Future developments could focus on integrating additional accessibility features and expanding support for multi-language publications, making the template even more inclusive.

Conclusion

The paper successfully delineates the features, rules, and guidelines associated with ACM's consolidated LaTeX template. By adhering to this template, authors can ensure their submissions meet ACM's stringent standards, facilitating smoother publication processes and enhancing the discoverability and accessibility of their research. As the landscape of digital publishing continues to evolve, tools like ACM's consolidated template will be pivotal in supporting the dissemination of scholarly work in a systematic and efficient manner.