GaRField++: Reinforced Gaussian Radiance Fields for Large-Scale 3D Scene Reconstruction (2409.12774v3)

Abstract: This paper proposes a novel framework for large-scale scene reconstruction based on 3D Gaussian splatting (3DGS) and aims to address the scalability and accuracy challenges faced by existing methods. For tackling the scalability issue, we split the large scene into multiple cells, and the candidate point-cloud and camera views of each cell are correlated through a visibility-based camera selection and a progressive point-cloud extension. To reinforce the rendering quality, three highlighted improvements are made in comparison with vanilla 3DGS, which are a strategy of the ray-Gaussian intersection and the novel Gaussians density control for learning efficiency, an appearance decoupling module based on ConvKAN network to solve uneven lighting conditions in large-scale scenes, and a refined final loss with the color loss, the depth distortion loss, and the normal consistency loss. Finally, the seamless stitching procedure is executed to merge the individual Gaussian radiance field for novel view synthesis across different cells. Evaluation of Mill19, Urban3D, and MatrixCity datasets shows that our method consistently generates more high-fidelity rendering results than state-of-the-art methods of large-scale scene reconstruction. We further validate the generalizability of the proposed approach by rendering on self-collected video clips recorded by a commercial drone.

• The paper introduces GaRField++, a framework that improves 3D scene reconstruction fidelity using reinforced Gaussian radiance fields and advanced partitioning techniques.
• It employs innovative methods such as ray-Gaussian intersection volume rendering and a ConvKAN-based decoupled appearance model to optimize training and visual quality.
• Experimental results demonstrate state-of-the-art performance across diverse datasets, highlighting its applications in AR/VR, city planning, and autonomous navigation.

GaRField++: Reinforced Gaussian Radiance Fields for Large-Scale 3D Scene Reconstruction

Introduction

The paper "GaRField++: Reinforced Gaussian Radiance Fields for Large-Scale 3D Scene Reconstruction" introduces a novel framework designed to address both scalability and accuracy challenges in 3D scene reconstruction. The authors leverage 3D Gaussian splatting (3DGS) to enhance the rendering quality while maintaining efficient processing. By partitioning the large-scale scene and applying a composite array of reinforcement techniques, the method achieves state-of-the-art fidelity in rendering, especially apparent in extensive environments. This paper details the architecture and benefits of GaRField++, alongside the extensive evaluation validating its effectiveness.

Methodology

GaRField++ employs a divide-and-conquer approach for scene partitioning, followed by independent rendering and seamless merging. Key reinforcements introduced in GaRField++ include improved ray-Gaussian intersection volume rendering, enhanced Gaussian density control, and a novel color decoupling module using a convolutional Kernelized Attention Network (KAN) alongside CNNs. These methods collectively bolster the rendering fidelity and optimize the training process.

Scene Partitioning

The large-scale scene is divided into multiple cells using a Structure-from-Motion (SfM) module to generate a sparse point cloud and estimate initial camera poses. Visibility-based view selection ensures optimal illumination conditions and geometric visibility, thus enabling high-fidelity rendering by assigning relevant cameras and point-cloud candidates to each cell.

Cells Rendering

Each cell, represented by 3D Gaussian primitives, undergoes advanced ray-Gaussian intersection volume rendering. This method accentuates rendering fidelity by leveraging the property that opacity along the ray is monotonically increasing. Additionally, Gaussian density control strategies are employed to prevent blurriness and maintain detail integrity.

ConvKAN-based Decoupled Appearance Modeling

To address the inconsistencies in lighting conditions, a network architecture combining KAN and CNN is used to decouple appearance modeling. This approach integrates KAN to improve rendering quality without significantly increasing model complexity. Notably, the color decoupling module is discarded post-training to ensure real-time rendering efficiency.

Optimization

Optimizing the Gaussian model involves a composite loss function incorporating depth distortion loss, normal consistency loss, and an RGB loss adapted from 3DGS. This carefully designed loss function enhances the accuracy and stability of large-scale scene reconstruction, addressing the common pitfall of artifact formation.

Experimental Results

The GaRField++ framework demonstrates superior performance across multiple datasets such as Mill19, Urban3D, and MatrixCity, as well as a self-collected dataset using a DJI drone. Comparisons with state-of-the-art methods showcase GaRField++'s ability to produce high-fidelity rendering results, as evidenced by metrics such as SSIM, PSNR, and LPIPS.

Implications and Future Directions

The implications of GaRField++ are profound for applications in AR/VR, city planning, and autonomous navigation. The robust framework addresses the scaling challenges while enhancing rendering accuracy, therefore providing a reliable basis for large-scale 3D reconstructions. Future research could explore optimal solutions for camera visibility and coordinate partitioning, further refine hyper-parameter tuning for specific scenarios, and enhance point cloud accuracy. The potential to extend this research to other domains, such as dynamic scene reconstruction and 3D mesh extraction, opens additional avenues for exploration.

Conclusion

GaRField++ represents a significant advancement in large-scale scene reconstruction by integrating sophisticated techniques for partitioning, rendering, and appearance modeling. The framework’s efficacy is validated through extensive experimentation, demonstrating superior performance in both fidelity and computational efficiency. The authors successfully address existing limitations in large-scale 3D reconstruction, providing a robust and scalable solution that is adept at managing the complexities of real-world environments.