Novel View Pose Synthesis with Geometry-Aware Regularization for Enhanced 3D Gaussian Splatting
Project Goal
- Enhance the quality of 3D reconstruction
- Improve multi view consistency
- Incorporate geometry-aware loss terms for accurate surface reconstruction
Project Page
Detailed information about the project can be found in the project page above!
Project Overview
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I developed a method to enhance indoor 3D reconstruction with 3D Gaussian Splatting (3DGS) by generating novel view camera poses, refining them with DIFIX, and applying geometry-aware loss terms. This approach improved geometry accuracy, multi-view consistency, and reduced artifacts.
Contributions
- Novel view camera pose generation
- Expanded spatial coverage and ensured consistency between viewpoints.
- Removed artifacts in scenes rendered from novel view camera poses using DIFIX.
- Introduction of additional loss terms
- Added a perceptual LPIPS loss applied only to novel views to preserve not only pixel information but also structural details.
- Applied normal consistency loss and depth smoothness loss to all views to improve geometry reconstruction quality.
Results
| method | initial point# | PSNR↑ | SSIM↑ | Training time | frame# |
|---|---|---|---|---|---|
| 3DGS | 100000 | 20.423 | 0.856 | 2h 13m | 168 |
| 2DGS | 100000 | 19.219 | 0.828 | 2h 1m | 168 |
| 2DGS_novel | 100000 | 20.375 | 0.842 | 1h 59m | 208 |
| Ours_novel | 100000 | 21.605 | 0.861 | 2h 6m | 208 |
| Ours_novel_loss | 100000 | 21.675 | 0.862 | 3h 55m | 208 |
- Compared to 3DGS, our method achieved a PSNR improvement from 20.423 to 21.675 and an SSIM increase from 0.856 to 0.862.
- Applying our method to 2DGS also yielded higher scores, demonstrating its generalizability.
🧑💻 My Role: Conceived the research idea, designed the methodology, and carried out the entire implementation — including dataset preparation, novel view generation, loss function integration, and experimental evaluation — with advisory input from a doctoral researcher.
3dgs-quality-enhancement