GaussReg: Fast 3D Registration with Gaussian Splatting (2407.05254v1)

Abstract: Point cloud registration is a fundamental problem for large-scale 3D scene scanning and reconstruction. With the help of deep learning, registration methods have evolved significantly, reaching a nearly-mature stage. As the introduction of Neural Radiance Fields (NeRF), it has become the most popular 3D scene representation as its powerful view synthesis capabilities. Regarding NeRF representation, its registration is also required for large-scale scene reconstruction. However, this topic extremly lacks exploration. This is due to the inherent challenge to model the geometric relationship among two scenes with implicit representations. The existing methods usually convert the implicit representation to explicit representation for further registration. Most recently, Gaussian Splatting (GS) is introduced, employing explicit 3D Gaussian. This method significantly enhances rendering speed while maintaining high rendering quality. Given two scenes with explicit GS representations, in this work, we explore the 3D registration task between them. To this end, we propose GaussReg, a novel coarse-to-fine framework, both fast and accurate. The coarse stage follows existing point cloud registration methods and estimates a rough alignment for point clouds from GS. We further newly present an image-guided fine registration approach, which renders images from GS to provide more detailed geometric information for precise alignment. To support comprehensive evaluation, we carefully build a scene-level dataset called ScanNet-GSReg with 1379 scenes obtained from the ScanNet dataset and collect an in-the-wild dataset called GSReg. Experimental results demonstrate our method achieves state-of-the-art performance on multiple datasets. Our GaussReg is 44 times faster than HLoc (SuperPoint as the feature extractor and SuperGlue as the matcher) with comparable accuracy.

• The paper introduces the first method for 3D scene registration using Gaussian Splatting with a coarse-to-fine framework.
• It demonstrates a significant speed boost, achieving 44x faster performance than competing methods while maintaining competitive accuracy.
• The new benchmark datasets validate the method’s applicability across diverse indoor and outdoor scenes for real-time reconstruction.

GaussReg: Fast 3D Registration with Gaussian Splatting

GaussReg introduces a novel approach to the long-standing problem of 3D point cloud registration, leveraging the recent advancements in Neural Radiance Fields (NeRF) and Gaussian Splatting (GS). This paper meticulously tackles the inherent challenges of aligning large-scale 3D scenes represented by GS, providing a method that stands out for its speed and accuracy.

Key Contributions

GaussReg comprises several significant contributions:

1. First Exploration of 3D Scene Registration with GS: The paper pioneers the exploration of registering 3D scenes using Gaussian Splatting. Recognizing the complexity and potential of GS, the research navigates through uncharted territory to propose a practical solution.
2. Coarse-to-Fine Registration Framework:

The framework consists of two principal stages: - Coarse Registration: Builds on established point cloud registration approaches, transforming GS representations into point clouds and leveraging techniques like ICP and GeoTransformer for initial alignment. - Image-Guided Fine Registration: Innovatively utilizes rendered images from the GS to refine and enhance the accuracy of the registration through volumetric feature extraction. This step addresses the noisiness and potential distortion inherent in GS-derived point clouds.

1. Comprehensive Benchmark Support: The development of a new benchmark dataset, ScanNet-GSReg, derived from the ScanNet dataset, together with an in-the-wild dataset, GSReg, allows for an in-depth evaluation of the method across various scenarios.
2. Efficient and Accurate Algorithm: GaussReg demonstrates superior performance, being 44 times faster than HLoc (SuperPoint and SuperGlue) with competitive accuracy. The efficiency of the method makes it suitable for large-scale scene reconstruction, overcoming the resource-intensive barriers associated with traditional and NeRF-based registration approaches.

Results and Implications

Numerical and Comparative Performance

The paper presents robust numerical results highlighting GaussReg's efficacy:

• GaussReg was rigorously tested against benchmarks like HLoc, FGR, and REGTR on datasets like ScanNet-GSReg, Objaverse, and GSReg.
• In the ScanNet-GSReg dataset, GaussReg achieved superior performance in terms of RTE and RSE, with a full success ratio compared to HLoc's partial success. GaussReg also significantly reduced the computational time, exemplifying its practical advantage.
• On Objaverse, GaussReg without fine registration outperformed other methods, showcasing strong generalizability.
• The GSReg dataset evaluations confirmed the method's applicability to real-world indoor and outdoor scenes, further validating its cross-domain performance.

Implications for AI and 3D Scene Reconstruction

Practical Implications:

• GaussReg's ability to efficiently and accurately register 3D scenes bolsters the usability of GS in practical applications such as urban mapping, architectural design, and augmented reality.
• The framework's speed and accuracy could potentially enable real-time scene reconstruction, enhancing technologies that rely on dynamic and large-scale 3D modeling.

Theoretical Implications:

• This work advances the understanding of integrating image-based data with point cloud representations for precise 3D registration.
• The success of GaussReg encourages further research into hybrid methods that combine explicit and implicit representations, potentially guiding future developments in neural implicit fields and their applications.

Future Directions

While GaussReg presents substantial advancements, the paper acknowledges areas for future research. Fusion inconsistencies due to varying environmental conditions between capture sessions are one limitation that may require novel methods of combining or adjusting GS models. Additionally, extending the framework to handle more diverse and complex scenes with varying lighting conditions could further solidify its applicability.

Conclusion

GaussReg sets a new benchmark in the domain of 3D registration using Gaussian Splatting. Offering a blend of speed and precision, the method overcomes the limitations of traditional and contemporary approaches, effectively broadening the scope and capabilities of large-scale 3D scene reconstruction. This research not only paves the way for practical implementations but also opens up new avenues for theoretical exploration in the integration of image-guided processes with point cloud data.