| # 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. |
|
|
