Spherical Voronoi Radiance Eval

---
name: spherical-voronoi-radiance-eval
description: This evaluation probes a differentiable spherical Voronoi partition for modeling view-dependent appearance and reflections in 3D Gaussian Splatting. It measures novel-view synthesis reconstruction fidelity and rendering efficiency against established radiance field baselines across synthetic and real-world scenes. Use when the user wants to benchmark on Mip-NeRF360, DeepBlending, Tanks&Temples, NeRF-Synthetic, Ref-NeRF, GlossySynthetic, Ref-Real, or asks about evaluating this task. Reports PSNR.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2512.14180
  bibtex_key: disario2025sphericalvoronoi
  confidence: high
---

# spherical-voronoi-radiance-eval

> Spherical Voronoi: Directional Appearance as a Differentiable Partition of the Sphere — Francesco Di Sario et al. (2025) (arXiv:2512.14180, 2025)

## What this evaluates

This evaluation probes a differentiable spherical Voronoi partition for modeling view-dependent appearance and reflections in 3D Gaussian Splatting. It measures novel-view synthesis reconstruction fidelity and rendering efficiency against established radiance field baselines across synthetic and real-world scenes.

## Datasets

- **Mip-NeRF360** — total ?; splits: test (-1)
- **DeepBlending** — total ?; splits: test (-1)
- **Tanks&Temples** — total ?; splits: test (-1)
- **NeRF-Synthetic** — total ?; splits: test (-1)
- **Ref-NeRF** — total ?; splits: test (-1)
- **GlossySynthetic** — total ?; splits: test (-1)
- **Ref-Real** — total ?; splits: test (-1)

## Metrics

- `PSNR` **(primary)** — range: [0, 100]
  - Peak Signal-to-Noise Ratio in decibels, computed as 10 * log10(MAX^2 / MSE) between rendered and ground truth images.
- `SSIM` — range: [0, 1]
  - Structural Similarity Index measuring perceived structural changes between two images, averaged over local windows.
- `LPIPS` — range: [0, 1]
  - Learned Perceptual Image Patch Similarity using deep features to measure perceptual distance between images.

## Input / output format

**Input**: Training images with corresponding camera poses and 3D Gaussian parameters (position, covariance, opacity, color, view-dependent coefficients) used to render novel viewpoints.

**Output**: Rendered RGB images from specified novel camera viewpoints for direct pixel-wise and perceptual comparison against ground truth.

## Scoring recipe

```python
import torch
from torchvision.transforms.functional import psnr, ssim
from lpips import LPIPS

def compute_metrics(rendered, ground_truth):
    rendered = rendered.clamp(0, 1)
    ground_truth = ground_truth.clamp(0, 1)
    p = psnr(rendered, ground_truth, data_range=1.0)
    s = ssim(rendered, ground_truth, data_range=1.0)
    l = lpips_model(rendered, ground_truth)
    return p, s, l
```

## Common pitfalls

- Baselines use test-set feedback for early stopping (every 500 iterations), while the proposed method uses fixed iteration training without test-set feedback, creating an asymmetric evaluation protocol.
- Standard photometric and perceptual metrics (PSNR, SSIM, LPIPS) may not fully capture qualitative improvements in high-frequency specular reflections and local light interactions.
- Memory and parameter counts are reported per Gaussian, but total scene memory scales with the number of Gaussians, which varies across datasets.

## Evidence (verbatim from paper)

> Our Spherical Voronoi formulation yields consistent improvements over all baselines, achieving higher PSNR than all other color parameterizations (SH, SG, SB).

## Citation

```bibtex
@misc{disario2025sphericalvoronoi,
  title={Spherical Voronoi: Directional Appearance as a Differentiable Partition of the Sphere},
  author={Francesco Di Sario et al. (2025)},
  year={2025},
  note={arXiv:2512.14180}
}
```

- arXiv: 2512.14180