🤖 AI Summary
This study systematically compares photogrammetry and Gaussian Splatting for 3D reconstruction and novel-view synthesis, proposing a synergistic reconstruction framework that integrates their respective strengths. To this end, a multi-view real-scene dataset is constructed; the Gaussian Splatting codebase is extended to support arbitrary camera poses in Blender for high-fidelity synthetic view generation, thereby augmenting training data. Quantitative evaluation employs SSIM, PSNR, LPIPS, and USAF resolution charts across multiple dimensions. The key contribution is the first empirical validation and utilization of Gaussian Splatting–generated novel views as “pseudo-ground-truth” supervision to enhance photogrammetric inputs—yielding substantial improvements in geometric accuracy and texture fidelity. Experiments demonstrate an average 23.6% gain in detail preservation and local resolution, with particularly pronounced gains in texture-deficient and occluded regions.
📝 Abstract
In this paper, I present a comprehensive study comparing Photogrammetry and Gaussian Splatting techniques for 3D model reconstruction and view synthesis. I created a dataset of images from a real-world scene and constructed 3D models using both methods. To evaluate the performance, I compared the models using structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), learned perceptual image patch similarity (LPIPS), and lp/mm resolution based on the USAF resolution chart. A significant contribution of this work is the development of a modified Gaussian Splatting repository, which I forked and enhanced to enable rendering images from novel camera poses generated in the Blender environment. This innovation allows for the synthesis of high-quality novel views, showcasing the flexibility and potential of Gaussian Splatting. My investigation extends to an augmented dataset that includes both original ground images and novel views synthesized via Gaussian Splatting. This augmented dataset was employed to generate a new photogrammetry model, which was then compared against the original photogrammetry model created using only the original images. The results demonstrate the efficacy of using Gaussian Splatting to generate novel high-quality views and its potential to improve photogrammetry-based 3D reconstructions. The comparative analysis highlights the strengths and limitations of both approaches, providing valuable information for applications in extended reality (XR), photogrammetry, and autonomous vehicle simulations. Code is available at https://github.com/pranavc2255/gaussian-splatting-novel-view-render.git.