OmniSplat: Taming Feed-Forward 3D Gaussian Splatting for Omnidirectional Images with Editable Capabilities (2412.16604v2)

Abstract: Feed-forward 3D Gaussian splatting (3DGS) models have gained significant popularity due to their ability to generate scenes immediately without needing per-scene optimization. Although omnidirectional images are becoming more popular since they reduce the computation required for image stitching to composite a holistic scene, existing feed-forward models are only designed for perspective images. The unique optical properties of omnidirectional images make it difficult for feature encoders to correctly understand the context of the image and make the Gaussian non-uniform in space, which hinders the image quality synthesized from novel views. We propose OmniSplat, a training-free fast feed-forward 3DGS generation framework for omnidirectional images. We adopt a Yin-Yang grid and decompose images based on it to reduce the domain gap between omnidirectional and perspective images. The Yin-Yang grid can use the existing CNN structure as it is, but its quasi-uniform characteristic allows the decomposed image to be similar to a perspective image, so it can exploit the strong prior knowledge of the learned feed-forward network. OmniSplat demonstrates higher reconstruction accuracy than existing feed-forward networks trained on perspective images. Our project page is available on: https://robot0321.github.io/omnisplat/index.html.

• The paper introduces a feed-forward approach using Yin-Yang grid decomposition to correct distortions in omnidirectional images.
• It employs a novel cross-attention mechanism and rasterizer to enhance feature extraction and maintain spatial consistency in 3D reconstructions.
• Experimental results show superior PSNR and SSIM performance with reduced processing times, enabling precise and editable 3D scene reconstructions.

Overview of "OmniSplat: Taming Feed-Forward 3D Gaussian Splatting for Omnidirectional Images with Editable Capabilities"

This paper presents a novel approach to the challenge of reconstructing and editing 3D scenes from omnidirectional images, using a technique called OmniSplat. The method extends the concept of 3D Gaussian Splatting (3DGS), which is known for rapid scene generation from sparse image inputs, to the domain of omnidirectional imaging. OmniSplat capitalizes on the extensive field of view provided by omnidirectional cameras, overcoming the limitations posed by traditional feed-forward networks that are typically optimized for standard perspective images.

Key Contributions

OmniSplat's primary contribution lies in its ability to generate high-quality 3D scenes from omnidirectional images using a novel framework leveraging the Yin-Yang grid decomposition. The technique addresses the typical challenges associated with processing omnidirectional data, such as distortion and irregular sampling inherent in equirectangular projections. The Yin-Yang grid offers a quasi-uniform representation that facilitates efficient use of conventional convolutional neural networks (CNNs) without complex restructuring.

1. Yin-Yang Grid Decomposition:
   • By slicing the sphere into two overlapping images based on the Yin-Yang grid system, this approach mitigates distortions and aligns the representation closely with perspective images' processing pipelines.
2. Cross-Attention Mechanism:
   • The authors introduce an innovative cross-attenuation procedure leveraging the Yin-Yang grid to capture relational depth information across omnidirectional views. This mechanism allows the network to maintain high fidelity in feature extraction and spatial consistency.
3. Yin-Yang Rasterizer:
   • This novel rasterizer is capable of synthesizing output omnidirectional images by mapping reconstructed 3D Gaussian parameters back to the sphere surface, thus producing high-quality final images with enhanced sampling uniformity.
4. Semantic Segmentation for 3D Editing:
   • OmniSplat incorporates an attention-driven segmentation mapping that supports consistent labeling across multiple views, enabling precise 3D editing.

Experimental Results

The paper includes a comprehensive assessment of OmniSplat using several omnidirectional datasets. The results demonstrate superior performance over existing methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), with significantly reduced processing times compared to optimization-heavy techniques like ODGS.

The enhancements, especially in the initial feed-forward predictions followed by optional brief optimizations, emphasize a robust trade-off between quality and computational efficiency. Notably, OmniSplat shows the highest reconstruction speed while maintaining high PSNR levels across various test setups.

Implications and Future Prospects

OmniSplat offers a powerful solution for applications requiring rapid and high-fidelity 3D reconstructions from omnidirectional images, such as virtual reality and autonomous navigation, where scene comprehension is crucial. The research bridges a crucial gap between 3DGS technology and omnidirectional data, opening up new avenues for deploying advanced CNN architectures to handle vast fields of view more efficiently.

Future research can build upon this framework to further enhance the robustness and quality of 3DGS amidst diverse environmental lighting and dynamic object movement. Additionally, exploring the integration of OmniSplat with depth-sensing modalities could further refine its application in depth-aware scene reconstructions.

In conclusion, "OmniSplat" demonstrates a significant advancement in the field of 3D scene reconstruction from omnidirectional images, highlighting the potential of combining innovative grid systems with cutting-edge computational techniques to overcome conventional limitations. The incorporation of a versatile editing framework further solidifies its standing as a tool for both real-time applications and complex post-processing tasks in interactive and automated systems.