Improving Geometry in Sparse-View 3DGS via Reprojection-based DoF Separation (2412.14568v1)

Abstract: Recent learning-based Multi-View Stereo models have demonstrated state-of-the-art performance in sparse-view 3D reconstruction. However, directly applying 3D Gaussian Splatting (3DGS) as a refinement step following these models presents challenges. We hypothesize that the excessive positional degrees of freedom (DoFs) in Gaussians induce geometry distortion, fitting color patterns at the cost of structural fidelity. To address this, we propose reprojection-based DoF separation, a method distinguishing positional DoFs in terms of uncertainty: image-plane-parallel DoFs and ray-aligned DoF. To independently manage each DoF, we introduce a reprojection process along with tailored constraints for each DoF. Through experiments across various datasets, we confirm that separating the positional DoFs of Gaussians and applying targeted constraints effectively suppresses geometric artifacts, producing reconstruction results that are both visually and geometrically plausible.

• The paper introduces a reprojection-based DoF separation method that constrains Gaussian movements to prevent overfitting and preserve structural integrity.
• The method is validated on datasets such as Mip-NeRF 360 and Tanks and Temples, achieving higher patch-wise Pearson correlation and competitive PSNR metrics.
• The findings highlight future research directions in addressing camera pose estimation errors and specular surface challenges in learning-based MVS systems.

Improving Geometry in Sparse-View 3DGS via Reprojection-based DoF Separation

The paper "Improving Geometry in Sparse-View 3DGS via Reprojection-based DoF Separation" tackles the challenges associated with enhancing geometric fidelity in sparse-view 3D reconstruction, especially when using learning-based Multi-View Stereo (MVS) models and subsequent refinement with 3D Gaussian Splatting (3DGS). Recent advancements in deep learning have improved MVS models significantly, enabling more efficient and effective 3D geometry extraction from limited view datasets. However, a persistent challenge arises during the refinement phase using 3DGS, where excessive positional degrees of freedom (DoFs) can introduce geometric artifacts as Gaussians overfit to texture patterns instead of maintaining structural integrity.

The authors propose a novel method that emphasizes the separation of DoFs based on uncertainty, distinguishing between image-plane-parallel DoFs, which are observed with lower uncertainty, and ray-aligned DoFs, which possess higher uncertainty due to their reliance on multi-view information. By introducing a reprojection-based DoF separation method, they enforce targeted constraints that limit excessive movements of Gaussians, thereby preserving both the rendering quality and enhancing geometric fidelity.

Through comprehensive experimental evaluations across multiple datasets, including Mip-NeRF 360, MVImgNet, and the Tanks and Temples benchmark, the paper demonstrates the efficacy of their method. Notably, they achieve higher patch-wise Pearson correlation values, indicative of more plausible geometric representations while maintaining competitive image fidelity metrics like PSNR. The framework's superiority over traditional baselines, exemplified by InstantSplat, is especially pronounced in geometric reconstruction scenarios with sparse views.

Posterior analyses highlight potential areas for future research, including challenges related to camera pose estimation errors and the interpretation of specular surfaces, as these factors can impact geometry plausibility in learning-based MVS outputs. The paper posits that future developments should explore more accurate global alignment and deepen our understanding of specular surface geometry to further enhance the robustness of learning-based MVS systems.

In conclusion, the method outlined provides a significant step forward in addressing the balance between rendering quality and geometric accuracy in sparse-view reconstructions. Careful separation and management of DoFs enable more refined and realistic geometric outputs, laying the groundwork for more advanced developments in fields such as augmented reality and autonomous systems where accurate 3D reconstructions are paramount.