## Image Comparison: Hierarchical-3DGS vs. FLOD-3DGS
### Overview
The image presents a comparative analysis of two 3D Gaussian Splatting (3DGS) methods: Hierarchical-3DGS and FLOD-3DGS. It showcases the visual results, memory usage, and Peak Signal-to-Noise Ratio (PSNR) for each method under varying levels of detail (τ = 120, τ = 30, τ = 15, τ = 0 (Max)). Two different scenes are used for the comparison: a truck and a cityscape.
### Components/Axes
* **Rows:**
* Row 1 & 3: Hierarchical-3DGS
* Row 2 & 4: FLOD-3DGS
* **Columns:**
* Column 1: τ = 120
* Column 2: τ = 30
* Column 3: τ = 15
* Column 4: τ = 0 (Max)
* **Metrics:**
* Memory Usage (GB)
* Memory Usage (%)
* PSNR (Peak Signal-to-Noise Ratio)
* **Scenes:**
* Scene 1: Truck
* Scene 2: Cityscape
### Detailed Analysis or ### Content Details
**Scene 1: Truck**
* **Hierarchical-3DGS:**
* τ = 120: memory: 2.70GB (65%), PSNR: 19.72
* τ = 30: memory: 3.15GB (76%), PSNR: 22.99
* τ = 15: memory: 3.58GB (86%), PSNR: 24.40
* τ = 0 (Max): memory: 4.15GB (100%), PSNR: 25.78
* **FLOD-3DGS:**
* level{3,2,1}: memory: 0.52GB (38%), PSNR: 23.30
* level{4,3,2}: memory: 0.59GB (43%), PSNR: 24.76
* level{5,4,3}: memory: 0.75GB (54%), PSNR: 25.32
* level5 (Max): memory: 1.37GB (100%), PSNR: 25.98
**Scene 2: Cityscape**
* **Hierarchical-3DGS:**
* τ = 120: memory: 3.14GB (69%), PSNR: 24.10
* τ = 30: memory: 3.60GB (79%), PSNR: 27.38
* τ = 15: memory: 3.98GB (87%), PSNR: 28.75
* τ = 0 (Max): memory: 4.57GB (100%), PSNR: 30.22
* **FLOD-3DGS:**
* level{3,2,1}: memory: 0.54GB (49%), PSNR: 27.60
* level{4,3,2}: memory: 0.60GB (55%), PSNR: 28.76
* level{5,4,3}: memory: 0.68GB (63%), PSNR: 29.84
* level5 (Max): memory: 1.09GB (100%), PSNR: 31.17
### Key Observations
* **Memory Usage:** FLOD-3DGS consistently uses significantly less memory than Hierarchical-3DGS across all levels of detail and both scenes.
* **PSNR:** PSNR values generally increase as the level of detail increases (τ decreases or level increases) for both methods and scenes, indicating improved image quality.
* **Scene Dependence:** Both methods exhibit different memory usage and PSNR values depending on the scene, suggesting scene complexity influences performance.
### Interpretation
The data suggests that FLOD-3DGS is more memory-efficient than Hierarchical-3DGS while achieving comparable or even better PSNR values, particularly in the cityscape scene. This indicates that FLOD-3DGS may offer a better trade-off between memory consumption and image quality. The increasing PSNR with increasing detail levels demonstrates the expected behavior of both methods, where finer details lead to improved image reconstruction quality. The scene dependence highlights the importance of considering scene characteristics when evaluating the performance of 3DGS methods.
