ODGS: 3D Scene Reconstruction from Omnidirectional Images with 3D Gaussian Splattings (2410.20686v1)

Abstract: Omnidirectional (or 360-degree) images are increasingly being used for 3D applications since they allow the rendering of an entire scene with a single image. Existing works based on neural radiance fields demonstrate successful 3D reconstruction quality on egocentric videos, yet they suffer from long training and rendering times. Recently, 3D Gaussian splatting has gained attention for its fast optimization and real-time rendering. However, directly using a perspective rasterizer to omnidirectional images results in severe distortion due to the different optical properties between two image domains. In this work, we present ODGS, a novel rasterization pipeline for omnidirectional images, with geometric interpretation. For each Gaussian, we define a tangent plane that touches the unit sphere and is perpendicular to the ray headed toward the Gaussian center. We then leverage a perspective camera rasterizer to project the Gaussian onto the corresponding tangent plane. The projected Gaussians are transformed and combined into the omnidirectional image, finalizing the omnidirectional rasterization process. This interpretation reveals the implicit assumptions within the proposed pipeline, which we verify through mathematical proofs. The entire rasterization process is parallelized using CUDA, achieving optimization and rendering speeds 100 times faster than NeRF-based methods. Our comprehensive experiments highlight the superiority of ODGS by delivering the best reconstruction and perceptual quality across various datasets. Additionally, results on roaming datasets demonstrate that ODGS restores fine details effectively, even when reconstructing large 3D scenes. The source code is available on our project page (https://github.com/esw0116/ODGS).

• The paper introduces a novel ODGS pipeline that accelerates 3D scene reconstruction from omnidirectional images.
• It employs 3D Gaussian splatting to project panoramic data onto tangent planes, greatly reducing distortion and computational time.
• The model achieves superior reconstruction quality with 100x speed improvements, advancing applications in VR, MR, and robotics.

Essay on "ODGS: 3D Scene Reconstruction from Omnidirectional Images with 3D Gaussian Splatting"

The paper "ODGS: 3D Scene Reconstruction from Omnidirectional Images with 3D Gaussian Splatting" presents a sophisticated approach for rapid and high-quality 3D scene reconstruction utilizing omnidirectional images. This paper contributes to the burgeoning field of computer vision and 3D reconstruction by employing a novel pipeline for transforming 360-degree image data into detailed 3D models using 3D Gaussian splatting (3DGS).

Key Contributions and Methodology

This research is situated in the context of overcoming the limitations encountered by traditional neural radiance fields (NeRFs) in processing omnidirectional images. NeRF approaches, while successful in generating high-quality reconstructions, suffer from protracted training and rendering times. Enter 3D Gaussian splatting, offering faster optimization and real-time rendering. However, applying standard perspective rasterization approaches directly to 360-degree images results in significant distortion due to their distinct optical characteristics.

The paper introduces ODGS, a rasterization pipeline specifically designed for panoramic image data, harnessing geometric interpretations that accommodate the unique attributes of spherical imagery. The authors propose a methodology where each Gaussian in the scene is projected onto tangent planes that interface with the unit sphere—an innovative step that significantly mitigates projection distortion.

A stark advantage of the ODGS approach is its computational efficiency. Leveraging the CUDA parallelization framework, the authors report a remarkable speed increase, reducing optimization and rendering times by a factor of 100 compared to NeRF-based methods. Furthermore, ODGS shows improved reconstruction and perceptual quality across both synthetic and real-world datasets, with superior performance on roaming datasets where large 3D scenes are involved. This achievement marks a methodological leap in handling the inherently large field of view presented by omnidirectional images.

Numerical Results and Implications

Empirical evaluations presented in the paper underscore ODGS’s efficacy. The model outperforms comparable state-of-the-art methods in terms of key metrics like PSNR, SSIM, and LPIPS, both in short (10 minutes) and extended (100 minutes) optimization durations. These advancements suggest that ODGS is not only a viable alternative but a preferable choice for scenarios demanding rapid, high-definition rendering of complex scenes.

Furthermore, the implementation of this technique allows for enhanced adaptability in virtual reality (VR) and mixed reality (MR) applications, where the demand for real-time processing and high fidelity is paramount. The ability to reconstruct entire scenes efficiently holds great potential for applications ranging from robotics to filmmaking.

Theoretical and Practical Implications

Theoretically, ODGS contributes to redefining how 3D spatial information can be interpreted from omnidirectional visual data. By marrying geometric interpretations with high-speed computational techniques, this paper sets a course for future explorations into efficient 3D representation methods.

Practically, the success of this approach opens avenues for improved interaction between users and digital environments, facilitating advancements in spatial computing, interactive media, and precise environmental modeling. The combination of speed and quality could transform current workflows in content creation and environmental analysis.

Future Directions

Looking ahead, the paper suggests potential improvements in modeling projected Gaussians, focusing on curbing artifacts by introducing more accurate distributions tailored to spherical surfaces. Expanding the CUDA framework for even broader application across GPU architectures also presents an exciting prospect for future research.

In summary, "ODGS: 3D Scene Reconstruction from Omnidirectional Images with 3D Gaussian Splatting" is a pivotal contribution to the field of 3D reconstruction, emphasizing computational efficiency and enhanced visual quality. It establishes a vital foundation for future research to innovate upon and adapt for an even wider array of advanced imaging applications.