Creating Seamless 3D Maps Using Radiance Fields (2403.11364v1)

Abstract: It is desirable to create 3D object models and 3D maps from 2D input images for applications such as navigation, virtual tourism, and urban planning. The traditional methods of creating 3D maps, (such as photogrammetry), require a large number of images and odometry. Additionally, traditional methods have difficulty with reflective surfaces and specular reflections; windows and chrome in the scene can be problematic. Google Road View is a familiar application, which uses traditional methods to fuse a collection of 2D input images into the illusion of a 3D map. However, Google Road View does not create an actual 3D object model, only a collection of views. The objective of this work is to create an actual 3D object model using updated techniques. Neural Radiance Fields (NeRF[1]) has emerged as a potential solution, offering the capability to produce more precise and intricate 3D maps. Gaussian Splatting[4] is another contemporary technique. This investigation compares Neural Radiance Fields to Gaussian Splatting, and describes some of their inner workings. Our primary contribution is a method for improving the results of the 3D reconstructed models. Our results indicate that Gaussian Splatting was superior to the NeRF technique.

• The paper demonstrates that integrating a novel pre-processing technique with NeRF and Gaussian Splatting significantly enhances 3D reconstruction accuracy.
• It details a dual-stage neural network approach in NeRF and an iterative optimization strategy in Gaussian Splatting to refine reconstruction details.
• Experimental findings reveal that Gaussian Splatting produces sharper, artifact-free models and superior real-time rendering in complex urban environments.

Enhancing 3D Reconstruction: A Comparative Study of NeRF and Gaussian Splatting Techniques

Introduction

The advancement in 3D reconstruction has led to significant breakthroughs in how we create 3D models from 2D images. Among the various methods employed for 3D reconstruction, Neural Radiance Fields (NeRF) and Gaussian Splatting have emerged as front runners, each with its unique approach to transforming 2D input into 3D output. This paper presents a comparative analysis of these two methodologies, focusing on their underlying principles, efficiency, and output quality. Furthermore, it introduces an innovative pre-processing technique aimed at enhancing the accuracy of the reconstructed models, especially when dealing with complex scenes like urban environments.

Neural Radiance Fields (NeRF)

NeRF represents a scene using a continuous 5D function, mapping a 3D position and viewing direction to color and volume density. By employing two neural networks — coarse and fine — NeRF first identifies regions with significant volume density before refining the details in these regions. This dual-step process, while innovative, has been noted for its intensive computational demands and the challenges it faces with real-time rendering and scalability.

Gaussian Splatting

On the other hand, Gaussian Splatting introduces an alternative approach. It begins with a structure from motion algorithm to estimate a point cloud from a collection of images, later converting this into a set of 3D Gaussians. An iterative optimization refines these Gaussians, adjusting their parameters to better match the original images. The technique stands out for its real-time rendering capabilities, facilitated by the use of rasterization over ray tracing, which is commonly employed in NeRF.

Methodological Comparison

The research involves the systematic collection of 2D images, followed by the application of both NeRF and Gaussian Splatting to generate 3D models. The process includes a novel dataset pre-processing technique to eliminate blurry images, utilizing Laplacian variance as a metric to measure sharpness. This pre-processing aims to enhance the quality of the training data, potentially leading to more accurate 3D reconstructions.

Experimental Findings

The findings from the comparative analysis reveal Gaussian Splatting's superiority in terms of both efficiency and output quality. Despite requiring more significant computational resources, Gaussian Splatting produced sharper 3D reconstructions with fewer artifacts than NeRF. Particularly in urban and complex scenes, Gaussian Splatting demonstrated an enhanced ability to maintain structural integrity and detail accuracy.

Future Directions

Looking ahead, further developments could focus on refining Gaussian Splatting's handling of camera movements and optimizing memory usage to extend its applicability to even larger scenes. Additionally, exploring synergies with generative AI techniques, such as stable diffusion, may offer avenues for enhancing the aesthetic qualities of the rendered 3D models.

Conclusion

This paper underscores the potential of Gaussian Splatting as a robust alternative to NeRF for 3D reconstruction tasks, especially in scenarios demanding real-time rendering and high-detail preservation. While both methodologies have their strengths, the scalability, efficiency, and output quality of Gaussian Splatting position it as a promising tool for future applications in virtual reality, augmented reality, and beyond. The introduction of an effective pre-processing step further reinforces the significance of quality input data in achieving optimal 3D rendering results, laying the groundwork for more sophisticated and accurate reconstruction techniques in the years to come.