Neusample Eval

---
name: neusample-eval
description: Evaluates the efficiency and rendering quality of a neural sample field for novel view synthesis. It probes how well a model can learn ray sampling distributions to reduce computation cost while maintaining high-fidelity image reconstruction compared to baseline NeRF methods. Use when the user wants to benchmark on Realistic Synthetic 360°, Real Forward-Facing, or asks about evaluating this task. Reports PSNR.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2111.15552
  bibtex_key: fang2021neusample
  confidence: high
---

# neusample-eval

> NeuSample: Neural Sample Field for Efficient View Synthesis — Fang et al. (2021) (arXiv:2111.15552, 2021)

## What this evaluates

Evaluates the efficiency and rendering quality of a neural sample field for novel view synthesis. It probes how well a model can learn ray sampling distributions to reduce computation cost while maintaining high-fidelity image reconstruction compared to baseline NeRF methods.

## Datasets

- **Realistic Synthetic 360°** — total 8; splits: train (100), test (200)
- **Real Forward-Facing** — total 8; splits: train (-1), test (-1)

## Metrics

- `PSNR` **(primary)** — range: other
  - Peak Signal-to-Noise Ratio computed in dB between rendered and ground-truth images. Higher is better.
- `SSIM` — range: [0, 1]
  - Structural Similarity Index measuring perceived quality of reconstructed images. Ranges from 0 to 1, higher is better.
- `LPIPS` — range: [0, 1]
  - Learned Perceptual Image Patch Similarity using deep features to measure perceptual distance. Lower is better.

## Input / output format

**Input**: Ray origins and directions (with 10-frequency positional encoding), camera poses, and training images at specified resolutions (800x800 or 1008x756).

**Output**: Rendered RGB images (pixel colors) for each test camera pose.

## Scoring recipe

```python
def compute_metrics(rendered_img, gt_img):
    psnr = 10 * log10(1.0 / mse(rendered_img, gt_img))
    ssim = structural_similarity(rendered_img, gt_img, data_range=1.0)
    lpips = perceptual_loss(rendered_img, gt_img)
    return psnr, ssim, lpips
# Average per scene, then across scenes.
```

## Common pitfalls

- NeRF-ID uses a massive batch size (66k on 16 TPUs) that artificially inflates PSNR; direct comparison without noting this is unfair.
- Resolution settings vary (1008x756 vs 504x378); results are not directly comparable across resolutions without normalization.
- Sample count (192, 128, 64, 32) drastically changes computation cost; evaluating only at one sample count misses the quality-speed trade-off.

## Evidence (verbatim from paper)

> We use two datasets, Realistic Synthetic 360° and Real Forward-Facing, to evaluate our method... The Realistic Synthetic 360° dataset consists of 8 scenes and each one includes 100 views for training and 200 views for testing. We take all views with an 800×800 resolution. The Real Forward-Facing dataset contains 8 complex real-world scenes... Each scene includes 20 - 62 images. Following [17], we hold out 1/8 images for testing and the rest are for training. All images are at 1008×756 pixels for experiments if unspecified. We show main PSNR results in Tab.1... Table 4: Depth boost effectiveness study on the “fern” scene of the Real Forward-Facing dataset. | #Sample | Depth Boost | PSNR↑ | SSIM↑ | LPIPS↓ |

## Citation

```bibtex
@misc{fang2021neusample,
  title={NeuSample: Neural Sample Field for Efficient View Synthesis},
  author={Fang et al. (2021)},
  year={2021},
  note={arXiv:2111.15552}
}
```

- arXiv: 2111.15552