Coherent 3D Gaussian Splatting for Sparse Novel View Synthesis

Core Concepts

We propose a regularized optimization approach to enable 3D Gaussian Splatting (3DGS) for sparse input views. Our key idea is to introduce coherency to the 3D Gaussians during optimization by constraining their movement in 2D image space using an implicit decoder and total variation loss. We further leverage monocular depth and flow correspondences to initialize and regularize the 3D Gaussian representation, enabling high-quality texture and geometry reconstruction from extremely sparse inputs.

Abstract

The paper presents a novel approach to enable 3D Gaussian Splatting (3DGS) for sparse input views. The key contributions are:

Coherent 3D Gaussian Optimization:

Assign a single Gaussian to each pixel in the input images.
Constrain the movement of Gaussians using an implicit decoder to enforce single-view smoothness.
Apply total variation loss to encourage multiview smoothness of the reconstructed geometry.
Introduce a flow-based regularization to further constrain the Gaussian positions across views.

Depth-based Initialization:

Use monocular depth estimation to initialize the 3D Gaussians.
Optimize the scale and offset of the monocular depth to ensure consistency across views.
Initialize the Gaussian scale based on the depth to properly cover each pixel.

The proposed regularized optimization and depth-based initialization enable high-quality texture and geometry reconstruction from extremely sparse input views, outperforming state-of-the-art NeRF-based approaches. Additionally, the authors show that their approach can identify occluded regions and hallucinate realistic details in those areas.

Stats

"The field of 3D reconstruction from images has rapidly evolved in the past few years, first with the introduction of Neural Radiance Field (NeRF) and more recently with 3D Gaussian Splatting (3DGS)."
"3DGS works well for dense input images, the unstructured point-cloud like representation quickly overfits to the more challenging setup of extremely sparse input images (e.g., 3 images), creating a representation that appears as a jumble of needles from novel views."
"We demonstrate significant improvements compared to the state-of-the-art sparse-view NeRF-based approaches on a variety of scenes."

Quotes

"Our key idea is to introduce a structured Gaussian representation that can be controlled in 2D image space."
"We then constraint the Gaussians, in particular their position, and prevent them from moving independently during optimization."
"To support our regularized optimization, we propose an approach to initialize the Gaussians using monocular depth estimates at each input view."

Key Insights Distilled From

CoherentGS

by Avinash Pali... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19495.pdf

Deeper Inquiries

How could the proposed approach be extended to handle dynamic scenes or scenes with transparent/reflective objects

To extend the proposed approach to handle dynamic scenes or scenes with transparent/reflective objects, several modifications and additions can be made. For dynamic scenes, incorporating temporal information and motion estimation techniques can help capture the scene's evolution over time. This can involve using optical flow or other motion estimation algorithms to track object movements and update the Gaussian representations accordingly. Additionally, considering the temporal coherence of the scene and incorporating it into the optimization process can improve the reconstruction quality for dynamic scenes.
For scenes with transparent or reflective objects, handling transparency and reflections in the 3D Gaussian representation is crucial. One approach could be to introduce additional parameters in the Gaussian representation to model transparency and reflectivity. By adjusting the opacity and color attributes of the Gaussians, the model can better capture the interactions of light with transparent and reflective surfaces. Incorporating physics-based rendering principles, such as Fresnel equations for reflections and transparency, can further enhance the realism of the reconstructed scenes.

What other types of regularization or constraints could be explored to further improve the reconstruction quality for sparse input views

To further improve the reconstruction quality for sparse input views, exploring additional regularization or constraints can be beneficial. One approach could be to incorporate semantic information or priors about the scene structure into the optimization process. By leveraging semantic segmentation masks or object detection algorithms, the model can enforce constraints based on the expected layout of objects in the scene. This can help guide the optimization towards more accurate and semantically meaningful reconstructions.
Another potential regularization technique is to incorporate depth-aware constraints. By considering depth information from the input views and enforcing consistency in depth estimates across different views, the model can better capture the scene's 3D structure. Depth-aware regularization can help prevent depth inconsistencies and improve the overall geometry reconstruction quality.
Furthermore, exploring multi-scale representations or hierarchical constraints can also enhance the reconstruction quality. By incorporating information at different scales or levels of abstraction, the model can capture fine details while maintaining global scene coherence. Multi-scale regularization can help address issues related to overfitting and underfitting in sparse input settings.

How could the hallucinated details in the occluded regions be made more robust and consistent across different novel views

To make the hallucinated details in the occluded regions more robust and consistent across different novel views, several strategies can be employed. One approach is to incorporate texture synthesis techniques to generate realistic textures in the occluded regions. By leveraging texture synthesis algorithms or generative models, the model can hallucinate details that are visually coherent with the rest of the scene.
Additionally, enforcing consistency in the hallucinated details across different views can improve robustness. By incorporating constraints that encourage consistency in the hallucinated textures or details from different viewpoints, the model can produce more coherent and realistic results. This can involve using flow-based regularization to ensure that the hallucinated details align properly across different views and maintain spatial consistency.
Moreover, leveraging adversarial training or perceptual loss functions can help improve the quality and realism of the hallucinated details. By training the model to generate details that are indistinguishable from real data or that match perceptual features of the scene, the hallucinated details can be made more robust and visually consistent across novel views.