SmileSplat: Generalizable Gaussian Splats for Unconstrained Sparse Images (2411.18072v1)

Abstract: Sparse Multi-view Images can be Learned to predict explicit radiance fields via Generalizable Gaussian Splatting approaches, which can achieve wider application prospects in real-life when ground-truth camera parameters are not required as inputs. In this paper, a novel generalizable Gaussian Splatting method, SmileSplat, is proposed to reconstruct pixel-aligned Gaussian surfels for diverse scenarios only requiring unconstrained sparse multi-view images. First, Gaussian surfels are predicted based on the multi-head Gaussian regression decoder, which can are represented with less degree-of-freedom but have better multi-view consistency. Furthermore, the normal vectors of Gaussian surfel are enhanced based on high-quality of normal priors. Second, the Gaussians and camera parameters (both extrinsic and intrinsic) are optimized to obtain high-quality Gaussian radiance fields for novel view synthesis tasks based on the proposed Bundle-Adjusting Gaussian Splatting module. Extensive experiments on novel view rendering and depth map prediction tasks are conducted on public datasets, demonstrating that the proposed method achieves state-of-the-art performance in various 3D vision tasks. More information can be found on our project page (https://yanyan-li.github.io/project/gs/smilesplat)

• The paper introduces SmileSplat, which reconstructs accurate 3D radiance fields from sparse, unconstrained images without relying on known camera parameters.
• It employs pixel-aligned Gaussian surfels via a Siamese Vision Transformer and a bundle-adjusting module to jointly optimize both Gaussian and camera parameters.
• Extensive experiments demonstrate superior PSNR, SSIM, and LPIPS performance on synthetic and real-world datasets, highlighting its robustness and generalizability.

Generalizable Gaussian Splats for Sparse Image Processing: A Critical Overview

The paper "SmileSplat: Generalizable Gaussian Splats for Unconstrained Sparse Images" presents an innovative methodology for reconstructing 3D radiance fields using sparse multi-view images without relying on known camera parameters. This approach, termed SmileSplat, enhances the capability of Gaussian Splatting techniques to operate effectively in real-world scenarios where the availability of dense image sequences or precise camera calibrations is limited.

Methodological Advancements

SmileSplat introduces several novel contributions to the field of image-based 3D reconstruction. It leverages pixel-aligned Gaussian surfels, which offer a reduced degree of freedom and improved multi-view consistency compared to traditional 3D Gaussians. The surfels are predicted using a multi-head Gaussian regression decoder integrated with a Siamese Vision Transformer (ViT) backbone. This architecture allows SmileSplat to effectively extract geometric priors and estimate Gaussian surfel parameters with high accuracy.

A significant aspect of SmileSplat is its capacity to optimize both the Gaussian parameters and the camera parameters intrinsically and extrinsically. This optimization is enabled by the Bundle-Adjusting Gaussian Splatting module, which refines the Gaussian representations and camera poses based on photometric and geometric constraints. The joint optimization strategy ensures the establishment of high-quality, scaled Gaussian radiance fields, essential for achieving superior novel view rendering performance.

Experimental Results

The paper demonstrates the efficacy of SmileSplat through extensive experiments on both synthetic and real-world datasets, including RealEstate10K and ACID. SmileSplat achieves state-of-the-art performance compared to existing methods, both those requiring known camera parameters and those that do not. It offers significant improvements in metrics like PSNR, SSIM, and LPIPS, particularly in scenarios with small visual overlap between input images, showcasing its robustness and versatility.

SmileSplat performs well across scenes with varying degrees of image overlap, highlighting the reliability of its intrinsic and extrinsic estimation modules. It achieves superior depth map predictions and renders high-quality novel views, even when tested on unseen datasets like Replica and ICL-NUIM, indicating strong cross-dataset generalizability.

Practical and Theoretical Implications

Practically, SmileSplat's ability to function without predefined camera parameters makes it highly applicable in fields like robotics, where capturing dense view sets or calibrating cameras accurately can be challenging. Its capability to generalize from sparse inputs has potential benefits for applications in dynamic environments and where real-time interaction with 3D maps is essential.

Theoretically, the method expands the scope of Gaussian Splatting by integrating it with transformer-based architectures and optimizing key intrinsic and extrinsic parameters. This integration advances the understanding of how neural networks can be leveraged in novel ways to perform geometry-aware optimization in image processing.

Future Directions

The paper opens several avenues for future research. Exploring more advanced optimization techniques for faster convergence, incorporating additional sources of domain knowledge to further improve the accuracy of intrinsic and extrinsic estimations, and extending the methodology to handle dynamic scenes could be beneficial directions. Additionally, investigating the integration of SmileSplat with other fields, such as augmented reality or autonomous navigation, may yield practical innovations.

In conclusion, SmileSplat represents an important step forward in the domain of 3D scene reconstruction from sparse imagery, bringing both theoretical advancements and practical capabilities that could influence future research and applications in the field of computer vision and related areas.