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
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
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/ZuriPanoon 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
.dptfloat32 (metric meters, no scale factor). - PanoInfinigen —
nyu-visionx/PanoInfinigenon HuggingFace, three scene configs (indoor/nature/urban) procedurally rendered with Infinigen. Ships train / val / test; onlytestis mirrored here. The originalnormalsfield 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 barenumpyarray (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):
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.