SparseFusion: Distilling View-conditioned Diffusion for 3D Reconstruction (2212.00792v3)

Abstract: We propose SparseFusion, a sparse view 3D reconstruction approach that unifies recent advances in neural rendering and probabilistic image generation. Existing approaches typically build on neural rendering with re-projected features but fail to generate unseen regions or handle uncertainty under large viewpoint changes. Alternate methods treat this as a (probabilistic) 2D synthesis task, and while they can generate plausible 2D images, they do not infer a consistent underlying 3D. However, we find that this trade-off between 3D consistency and probabilistic image generation does not need to exist. In fact, we show that geometric consistency and generative inference can be complementary in a mode-seeking behavior. By distilling a 3D consistent scene representation from a view-conditioned latent diffusion model, we are able to recover a plausible 3D representation whose renderings are both accurate and realistic. We evaluate our approach across 51 categories in the CO3D dataset and show that it outperforms existing methods, in both distortion and perception metrics, for sparse-view novel view synthesis.

Insights into Author Guidelines for CVPR Proceedings

The provided document delineates the specific formatting and submission guidelines for manuscripts intended for the CVPR proceedings. This paper serves as a meticulous guide, ensuring that submissions adhere to established standards, thereby facilitating a consistent review and publication process within the scientific community focused on computer vision.

Content Overview

The document commences with a structured introduction to the submission process, emphasizing the critical modifications made to previous guidelines. It includes detailed instructions regarding language and manuscript length, stipulating that papers should not exceed eight pages excluding references. The insistence on strict adherence to formatting rules is evident, underscoring that deviations or attempts to alter margins will result in immediate rejection, which directly affects the reviewing process.

A notable inclusion is the guideline for incorporating a printed ruler on the version submitted for review. This requirement aims to enhance reviewer feedback by facilitating precise reference to specific lines within the manuscript.

Numerical and Technical Aspects

The guidelines advocate for precise mathematical notation and cross-referencing, mandating that all equations and sections must be numerically labeled. This ensures clarity and ease of reference in complex discussions. The document further elaborates on manuscript formatting, including margin settings, type styles, fonts, and the positioning of graphics and captions, preserving consistency throughout.

In addition to formatting, particular attention is given to the process of blind review. Authors are instructed on maintaining anonymity while citing their previous work, thus safeguarding the integrity of the review process.

Practical Implications

Following these guidelines ensures a streamlined submission experience that aligns with technological advancements and accessibility standards. Adherence not only promotes fairness and uniformity in the review but also enhances the readability and professionalism of submitted manuscripts.

The emphasis on color usage is particularly relevant, addressing accessibility issues faced by individuals with color vision deficiencies. Authors are encouraged to complement color-based distinctions with additional features to improve visual comprehension.

Theoretical Insights and Future Developments

The structured methodologies prescribed in the document reflect ongoing efforts to improve the transparency, reproducibility, and accessibility of scientific research. Responsive adjustments in guidelines illustrate the dynamic interplay between evolving publication standards and technological innovations.

Looking forward, the continued refinement of submission protocols could further enhance collaborative research efforts and dissemination. By establishing stringent yet clear standards, CVPR ensures that contributions from diverse institutions cohere into a unified, professional academic discourse.

In summary, this document serves as an integral resource for authors preparing manuscripts for CVPR. By outlining an exhaustive framework for submission, it reinforces the collective goal of maintaining high standards in the dissemination of cutting-edge research in computer vision and pattern recognition.