Towards Realistic Example-based Modeling via 3D Gaussian Stitching (2408.15708v1)

Abstract: Using parts of existing models to rebuild new models, commonly termed as example-based modeling, is a classical methodology in the realm of computer graphics. Previous works mostly focus on shape composition, making them very hard to use for realistic composition of 3D objects captured from real-world scenes. This leads to combining multiple NeRFs into a single 3D scene to achieve seamless appearance blending. However, the current SeamlessNeRF method struggles to achieve interactive editing and harmonious stitching for real-world scenes due to its gradient-based strategy and grid-based representation. To this end, we present an example-based modeling method that combines multiple Gaussian fields in a point-based representation using sample-guided synthesis. Specifically, as for composition, we create a GUI to segment and transform multiple fields in real time, easily obtaining a semantically meaningful composition of models represented by 3D Gaussian Splatting (3DGS). For texture blending, due to the discrete and irregular nature of 3DGS, straightforwardly applying gradient propagation as SeamlssNeRF is not supported. Thus, a novel sampling-based cloning method is proposed to harmonize the blending while preserving the original rich texture and content. Our workflow consists of three steps: 1) real-time segmentation and transformation of a Gaussian model using a well-tailored GUI, 2) KNN analysis to identify boundary points in the intersecting area between the source and target models, and 3) two-phase optimization of the target model using sampling-based cloning and gradient constraints. Extensive experimental results validate that our approach significantly outperforms previous works in terms of realistic synthesis, demonstrating its practicality. More demos are available at https://ingra14m.github.io/gs_stitching_website.

• The paper introduces a novel approach that seamlessly stitches multiple 3D Gaussian fields for enhanced texture and structural blending.
• It employs real-time segmentation, k-nearest neighbors for boundary detection, and a two-phase optimization to improve interactivity and model consistency.
• Experimental results show significant improvements over techniques like SeamlessNeRF, achieving higher photorealism in composite 3D models.

Towards Realistic Example-based Modeling via 3D Gaussian Stitching

"Towards Realistic Example-based Modeling via 3D Gaussian Stitching" by Xinyu Gao et al. addresses the challenges of realistic and seamless composition of 3D objects derived from real-world scenes. This work advances the landscape of example-based modeling in computer graphics by introducing a method that combines multiple Gaussian fields using sample-guided synthesis and a point-based representation, achieving superior seamless appearance blending. This essay provides an in-depth examination of the methodology and implications of this novel approach.

Methodology

The authors target the limitations of prior methods, such as SeamlessNeRF, which struggle with interactive editing and harmonious stitching in real-world scenarios due to gradient-based strategies and grid-based representations. They propose combining multiple Gaussian fields (3DGS) in a point-based format, offering improved interactivity and seamless texture blending.

The solution is delineated through three primary steps:

1. Real-time Segmentation and Transformation of Gaussian Models: Utilizing a graphical user interface (GUI), users can segment and transform Gaussian fields in real-time. This enables the segmentation of objects into meaningful parts that can be recomposed semantically to create new models.
2. Boundary Point Identification Using k-Nearest Neighbors (KNN) Analysis: The authors employ KNN analysis to identify boundary points between intersecting Gaussian fields. This step is crucial for establishing boundary conditions that will ensure harmonious texture blending.
3. Two-phase Optimization Scheme:
   • Sampling-based Cloning (S-phase): This phase involves propagating the style from boundary points to the entire model using a heuristic sampling strategy. It includes a novel gradient loss calculated in 2D screen space, which is shown to be more effective than straightforward 3D methods.
   • Clustering-based Tuning (T-phase): To address global appearance consistency issues that may arise from sampling-based cloning, the authors use an aggregation and clustering technique to tune the target field. This phase ensures the seamless propagation of color tones as well as structural characteristics.

Experimental Results

The experimental validation of this method demonstrates significant improvements over previous works, such as SeamlessNeRF, particularly in terms of realism and seamless synthesis of complex models from real-world data. The pipeline effectively preserves both the texture and structural integrity of composite models, which is critical for photorealistic rendering.

Key Findings and Quantitative Analysis

The authors performed an array of experiments involving 21 composite models, showcasing various complex scenarios where their method outperforms existing approaches. They conducted a qualitative comparison and employed Video Quality Assessment (VQA) methods for quantitative comparison, where their approach scored higher in terms of realism and consistency.

Discussion

This research has several practical and theoretical implications for the field of example-based modeling and neural rendering:

• Practical Implications: The interactive and real-time capabilities enabled by the GUI significantly enhance user experience. This advancement allows users to efficiently create complex, semantically meaningful models directly from real-world scenes.
• Theoretical Implications: The introduction of sampling-based cloning and clustering-based tuning provides a robust framework for blending not only textures but also structural characteristics. This represents a significant contribution to the methodologies employed in neural rendering and example-based modeling.

Future Work and Limitations

While the presented method achieves substantial improvements in real-world applications, it has limitations, such as its inability to perform non-rigid transformations, which are crucial for more flexible and imaginative model creation. Future work could explore integrating deformation methods, like ARAP, and enhancing lighting consistency across composed models to further refine the visual quality.

Conclusion

Xinyu Gao et al.'s work on "Towards Realistic Example-based Modeling via 3D Gaussian Stitching" substantially advances the capabilities of interactive example-based modeling. By leveraging a point-based representation and introducing innovative optimization techniques, it sets a new benchmark for creating intricate, realistic models from real-world scenes. This methodology holds significant promise for further developments in AI-driven neural rendering and computer graphics.

The presented approach not only addresses existing challenges but also opens up new avenues for research and applications in the domain of 3D model synthesis and composition.