Contranerf Eval

---
name: contranerf-eval
description: Evaluates generalizable Neural Radiance Field (NeRF) methods for novel view synthesis, specifically probing their ability to generalize from synthetic training data to real-world indoor and outdoor scenes. It measures rendering quality and geometric consistency across different domain gaps. Use when the user wants to benchmark on 3D-FRONT, ScanNet, DTU, LLFF, Google Scanned Object, or asks about evaluating this task. Reports PSNR.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2303.11052
  bibtex_key: yang2023contranerf
  confidence: high
---

# contranerf-eval

> ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-real Novel View Synthesis via Contrastive Learning — Hao Yang et al. (arXiv:2303.11052, 2023)

## What this evaluates

Evaluates generalizable Neural Radiance Field (NeRF) methods for novel view synthesis, specifically probing their ability to generalize from synthetic training data to real-world indoor and outdoor scenes. It measures rendering quality and geometric consistency across different domain gaps.

## Datasets

- **3D-FRONT** — total 88; splits: train (88)
- **ScanNet** — total 96; splits: train (88), test (8)
- **DTU** — total 4; splits: test (4)
- **LLFF** — total ?; splits: test (-1)
- **Google Scanned Object** — total ?; splits: train (-1)

## Metrics

- `PSNR` **(primary)** — range: dB / [0, 1]
  - Peak Signal-to-Noise Ratio, measuring pixel-wise reconstruction accuracy in decibels. Higher values indicate better fidelity.
- `SSIM` — range: [0, 1]
  - Structural Similarity Index, measuring perceptual structural similarity between rendered and ground truth images. Ranges from 0 to 1, where 1 is perfect match.
- `LPIPS` — range: [0, 1]
  - Learned Perceptual Image Patch Similarity, measuring perceptual distance using deep features. Lower values indicate higher perceptual similarity.

## Input / output format

**Input**: A set of source RGB images with known camera poses for novel view synthesis. For ScanNet, 10 neighboring input views are used. For DTU/LLFF, standard multi-view inputs are provided.

**Output**: Rendered RGB image of the novel view at specified resolution (640×480 for ScanNet, 800×600 for DTU, 1008×756 for LLFF).

## Scoring recipe

```python
def compute_metrics(rendered_img, gt_img):
    psnr = peak_signal_noise_ratio(gt_img, rendered_img)
    ssim = structural_similarity(gt_img, rendered_img, data_range=gt_img.max() - gt_img.min())
    lpips = lpips_loss(rendered_img, gt_img)
    return psnr, ssim, lpips
```

## Common pitfalls

- Synthetic-trained models produce sharper but geometrically inaccurate volume densities due to domain gap.
- Interpolation-based methods may artificially inflate color prediction performance because they directly copy input colors, masking domain shift.
- Fair comparison requires running all baselines on identical experiment settings using official code.

## Evidence (verbatim from paper)

> Following IBRNet, we evaluate all these methods using PSNR, SSIM, and LPIPS. We report PSNR/SSIM (higher is better) and LPIPS (lower is better).

## Citation

```bibtex
@misc{yang2023contranerf,
  title={ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-real Novel View Synthesis via Contrastive Learning},
  author={Hao Yang et al.},
  year={2023},
  note={arXiv:2303.11052}
}
```

- arXiv: 2303.11052