# Panorama Depth Benchmark

A unified evaluation suite for equirectangular (360°) depth estimation,
assembled from the test splits of eight public panorama depth datasets.
Every sample listed in `<dataset>/test.txt` has been copied into this
repository — the loader never reads anything outside this folder.

- **Modality:** equirectangular RGB + metric depth (+ validity mask on
  Holo360D / ZuriPano)
- **Total test samples:** 15 766 across 12 subsets (2 outdoor-only, the rest
  indoor / nature / urban)
- **Total size on disk:** ≈ 61 GB
- **License:** each subset keeps its original license; see the per-dataset
  rows below for canonical sources.

---

## Datasets

| key                 |     N | split | scene     | RGB ext | depth ext | depth → meters        | valid range (m) | mask file |
|---------------------|------:|:------|-----------|---------|-----------|-----------------------|-----------------|-----------|
| `stanford2d3d`      |   373 | test  | indoor    | png     | png u16   | `v / 512.0`           | 0.001 / 5.0   | —         |
| `matterport3d`      | 2 014 | test  | indoor    | jpg     | png u16   | `v / 4000.0`          | 0.001 / 5.0   | —         |
| `panosuncg`         | 3 944 | test  | synthetic | png     | png u8-RGB| `v[...,0] * 10 / 255` | 0.001 / 5.0   | —         |
| `structured3d`      | 1 691 | test  | synthetic | png     | png u16   | `v / 1000.0`          | 0.001 / 5.0   | —         |
| `metropolis`        | 3 000 | test  | outdoor   | jpg     | png u16   | `v / 512.0`           | 0.2  / 128.0  | —         |
| `zuripano`          |   100 | test  | outdoor   | parquet | parquet   | `v_u16 * 200/65535`   | 0.01 / 75.0   | ✓ (in parquet row) |
| `holo360d_indoor`   |   360 | test  | indoor    | jpg     | **EXR**   | first channel is metric | 0.01 / 10.0 | ✓ (`mask/`) |
| `holo360d_outdoor`  |    90 | test  | outdoor   | jpg     | **EXR**   | first channel is metric | 0.01 / 75.0 | ✓ (`mask/`) |
| `replica360_2k`     |   130 | test  | indoor    | jpg     | **.dpt**  | Sintel float32 (metric) | 0.001 / 10.0 | —         |
| `panoinfinigen_indoor`  | 4 392 | test | indoor  | png     | **.npy**  | raw float16 (metric)   | 0.001 / 10.0  | — |
| `panoinfinigen_nature`  |   848 | test | nature  | png     | **.npy**  | raw float16 (metric)   | 0.001 / 100.0 | — |
| `panoinfinigen_urban`   |   788 | test | urban   | png     | **.npy**  | raw float32 (metric)   | 0.001 / 500.0 | — |

### Depth range semantics

`valid range` above is the **validity mask** gate: a depth pixel is valid iff
`min < d ≤ max` and `d` is finite (AND-ed with the mask file where present).
These values are aligned with the `cvpr/benchmark` evaluation harness's
`valid_from_depth` so that both code paths select the same pixels.

Note that the cvpr harness additionally clamps both prediction and GT to a
(usually identical) `[min_depth, max_depth]` window when computing metrics.
For the seven datasets shared with that harness the two ranges coincide with
one exception worth knowing about: **Metropolis** uses a validity mask of
`(0.2, 128]` m (DA360's convention) but clamps metrics to `[0.01, 100]` m.

Datasets not present in the cvpr harness (`replica360_2k`, `panoinfinigen_*`)
carry ranges chosen for the data itself, not copied from it.

For datasets without a mask file, validity is derived from the depth map
alone (`min_depth < d ≤ max_depth` and finite). For Holo360D the `mask/`
JPEG also removes the photographer rig / tripod region (mask > 127 is
valid). ZuriPano's mask is stored inline in the parquet row (`mask`
field, L-mode PNG bytes).

Depth is stored as **radial distance** on all panorama datasets (not Z /
planar depth) — do not pre-wrap pano frames with a perspective-depth
model's output.

### Image resolution

Native sizes vary per dataset. The loader does not resize by default;
each row returns the native array. Common sizes:

- Stanford2D3D: 512 × 1024
- Matterport3D: 1024 × 2048
- PanoSUNCG: 1024 × 2048
- Structured3D: 512 × 1024
- Metropolis: 1024 × 2048
- Holo360D: 1024 × 2048 (RGB) / 1024 × 2048 (EXR)
- ZuriPano: 2048 × 4096
- Replica360-2k: 1024 × 2048
- PanoInfinigen (all configs): 1024 × 2048 (downsampled 2× from the 2048 × 4096 source;
  depth uses an exact `[::2, ::2]` stride so no interpolated values appear)

---

## Directory layout

```
benchmark/
├── stanford2d3d/
│   ├── test.txt          # <rgb_rel> <depth_rel>
│   ├── rgb/<stem>.png
│   └── depth/<stem>.png
├── matterport3d/
│   └── ...
├── holo360d_indoor/
│   ├── test.txt          # <rgb_rel> <depth_rel> <mask_rel>
│   ├── rgb/<stem>.jpg
│   ├── depth/<stem>.exr
│   └── mask/<stem>.jpg
├── zuripano/
│   └── data/test-{00000..00004}-of-00005.parquet
├── _loader.py            # PyTorch Dataset: load_dataset_torch(root, name)
├── benchmark.py          # HuggingFace datasets BuilderConfig
├── _collect.py           # (dev) regenerate the tree from source dirs
└── README.md
```

Stems are flattened from the original scene / room / frame hierarchy with
`__` as a separator, e.g. `area_5a__camera_<uuid>_hallway_1_frame_...`.
Each stem is globally unique within its dataset.

---

## Loading

### 1. Plain Python / NumPy

```python
from _loader import PanoDepthDataset
ds = PanoDepthDataset(root="/path/to/benchmark", name="stanford2d3d")
sample = ds[0]
# sample is a dict:
#   rgb:    uint8  [H, W, 3]
#   depth:  float32 [H, W]     metric distance in meters, radial
#   valid:  bool    [H, W]     after depth gating + optional mask
#   stem:   str                flattened id, unique within dataset
```

### 2. HuggingFace `datasets`

```python
from datasets import load_dataset
ds = load_dataset("/path/to/benchmark", "stanford2d3d", split="test")
```

The HF script loads bytes lazily (`Image(decode=False)`); call
`.cast_column("rgb", Image())` etc. if you want decoded arrays.

---

## Source attribution

- **Stanford2D3D** — Armeni et al., *Joint 2D-3D-Semantic Data for
  Indoor Scene Understanding*, 2017. (official 2D-3D-S release)
- **Matterport3D** — Chang et al., ICCV 2017. (panoramas provided by the
  PanDA benchmark re-release.)
- **PanoSUNCG** — Wang et al., SUNCG-based 360° renderings. Depth encoded
  into the R channel of an 8-bit PNG.
- **Structured3D** — Zheng et al., ECCV 2020. Only the panorama view and
  the raw-light rendering are used.
- **Metropolis** — DA360 (Depth-Anywhere) outdoor street-view set; see
  the DA360 project page.
- **ZuriPano** — PaGeR / `prs-eth/ZuriPano` on HuggingFace. 100 real
  outdoor LiDAR scans.
- **Holo360D** — a mixed real-world capture (12 indoor + 3 outdoor
  scenes); 360 / 90 frames `linspace`-sampled from each scene.
- **Replica360-2k** — 2K-resolution equirectangular renderings of the
  Replica indoor meshes (apartment / FRL / hotel / office / room). Depth
  stored as Sintel `.dpt` float32 (metric meters, no scale factor).
- **PanoInfinigen** — `nyu-visionx/PanoInfinigen` on HuggingFace, three
  scene configs (`indoor` / `nature` / `urban`) procedurally rendered with
  Infinigen. Ships train / val / test; **only `test`** is mirrored here.
  The original `normals` field is dropped (~75% of the source bytes and
  unused for depth evaluation), and the 4096 × 2048 frames are downsampled
  2× to 2048 × 1024. Depth is a bare `numpy` array (not an image file) with
  metric meters directly — float16 for indoor/nature, float32 for urban.

Please cite each original paper when using the corresponding subset.

---

## Regenerating

If a source dataset is updated or the split is extended, re-run from the
repo root (one dataset at a time, or all):

```bash
python _collect.py                     # all
python _collect.py structured3d        # one
```

`_collect.py` is idempotent (skips files already copied with the same
size) and writes a fresh `test.txt` each time.