hugsam/room_envelopes_model
Depth Estimation • Updated • 17 • 1
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: ValueError
Message: Protocol not known: memory
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1520, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 130, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1382, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1560, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
__key__ string | __url__ string | image image | depth image | normal image | layout_depth image | layout_normal image | layout_depth_rerendered image | layout_normal_rerendered image | seen_mask image | json dict |
|---|---|---|---|---|---|---|---|---|---|---|
ai_001_001-cam_00-0 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
"scene": "ai_001_001",
"camera": "cam_00",
"frame": 0,
"intrinsics": [
[
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0.5
],
[
0,
1.1547004995246841,
0.5
],
[
0,
0,
1
]
]
} | ||||||||
ai_001_001-cam_00-1 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
"scene": "ai_001_001",
"camera": "cam_00",
"frame": 1,
"intrinsics": [
[
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],
[
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0.5
],
[
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1
]
]
} | ||||||||
ai_001_001-cam_00-10 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
"scene": "ai_001_001",
"camera": "cam_00",
"frame": 10,
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],
[
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0.5
],
[
0,
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1
]
]
} | ||||||||
ai_001_001-cam_00-11 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
"scene": "ai_001_001",
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"frame": 11,
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[
0,
1.1547004995246835,
0.5
],
[
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]
} | ||||||||
ai_001_001-cam_00-12 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
"scene": "ai_001_001",
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0.5
],
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]
} | ||||||||
ai_001_001-cam_00-13 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
"scene": "ai_001_001",
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]
} | ||||||||
ai_001_001-cam_00-14 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
"scene": "ai_001_001",
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],
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]
} | ||||||||
ai_001_001-cam_00-15 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
"scene": "ai_001_001",
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} | ||||||||
ai_001_001-cam_00-16 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
"scene": "ai_001_001",
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]
} | ||||||||
ai_001_001-cam_00-17 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
"scene": "ai_001_001",
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]
} | ||||||||
ai_001_001-cam_00-18 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
"scene": "ai_001_001",
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]
} | ||||||||
ai_001_001-cam_00-19 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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} | ||||||||
ai_001_001-cam_00-2 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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]
} | ||||||||
ai_001_001-cam_00-20 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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} | ||||||||
ai_001_001-cam_00-21 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
"scene": "ai_001_001",
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]
} | ||||||||
ai_001_001-cam_00-22 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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} | ||||||||
ai_001_001-cam_00-23 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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ai_001_001-cam_00-24 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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} | ||||||||
ai_001_001-cam_00-25 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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} | ||||||||
ai_001_001-cam_00-26 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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} | ||||||||
ai_001_001-cam_00-27 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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} | ||||||||
ai_001_001-cam_00-28 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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} | ||||||||
ai_001_001-cam_00-29 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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} | ||||||||
ai_001_001-cam_00-3 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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ai_001_001-cam_00-30 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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} | ||||||||
ai_001_001-cam_00-31 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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ai_001_001-cam_00-32 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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} | ||||||||
ai_001_001-cam_00-33 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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} | ||||||||
ai_001_001-cam_00-34 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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ai_001_001-cam_00-35 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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ai_001_001-cam_00-37 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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ai_001_001-cam_00-38 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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ai_001_001-cam_00-39 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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ai_001_001-cam_00-4 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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ai_001_001-cam_00-40 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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ai_001_001-cam_00-41 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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ai_001_001-cam_00-42 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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ai_001_001-cam_00-43 | hf://datasets/hugsam/Room_Envelopes@f41b106dda941125869e20e8bcfa20acf72a21f7/data_wds/train-0000.tar | {
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End of preview.
Room Envelopes
See project page for details on this repository https://sambahrami.com/room_envelopes
Original dataset https://github.com/apple/ml-hypersim License https://creativecommons.org/licenses/by-nc-sa/3.0/
Modifications include creating point clouds from the Hypersim dataset and re-rendering depth maps with all objects removed, retaining the first structural layout surface for each example and its normal map. Additionally, rerendered versions of the layout depth and normal maps are provided, which exclude class 0 and use an improved rasterisation method for better visual quality.
This dataset is derived from the Hypersim dataset by Apple Inc., licensed under CC BY-NC-SA 3.0.
Usage
from datasets import load_dataset
# Load a specific split
ds = load_dataset("hugsam/Room_Envelopes", split="train")
# Or load all splits at once
ds = load_dataset("hugsam/Room_Envelopes")
# ds["train"], ds["validation"], ds["test"]
# Streaming (recommended for large datasets)
ds = load_dataset("hugsam/Room_Envelopes", split="train", streaming=True)
# Each sample is a dict:
sample = next(iter(ds))
sample["image"] # PIL Image — original rendered RGBA image (1024×768)
sample["depth"] # PIL Image — first-surface depth map (16-bit grayscale)
sample["normal"] # PIL Image — first-surface normal map (16-bit RGB)
sample["layout_depth"] # PIL Image — layout depth map (16-bit grayscale)
sample["layout_normal"] # PIL Image — layout-layer normal map (16-bit RGB)
sample["layout_depth_rerendered"] # PIL Image — rerendered layout depth (16-bit grayscale, no class 0)
sample["layout_normal_rerendered"] # PIL Image — rerendered layout normal (16-bit RGB, no class 0)
sample["seen_mask"] # PIL Image — visibility mask (8-bit grayscale)
sample["json"] # dict with metadata:
# sample["json"]["scene"] — scene identifier, e.g. "ai_001_001"
# sample["json"]["camera"] — camera identifier, e.g. "cam_00"
# sample["json"]["frame"] — frame number (int)
# sample["json"]["intrinsics"] — 3×3 camera intrinsics matrix (list of lists)
Dataset Structure
Each sample contains paired images of an indoor scene with all objects removed, leaving only the structural layout (walls, floor, ceiling):
| Field | Description | Format |
|---|---|---|
image |
Hypersim dataset image (tonemapped renders) | 1024×768 RGBA PNG |
depth |
Depth to the first visible structural surface | 1024×768 16-bit grayscale PNG |
normal |
Surface normal of the first visible structural surface | 1024×768 16-bit RGB PNG |
layout_depth |
Layout-layer depth map | 1024×768 16-bit grayscale PNG |
layout_normal |
Layout-layer surface normal map | 1024×768 16-bit RGB PNG |
layout_depth_rerendered |
Rerendered layout depth (no class 0, improved rasterisation) | 1024×768 16-bit grayscale PNG |
layout_normal_rerendered |
Rerendered layout normal (no class 0, improved rasterisation) | 1024×768 16-bit RGB PNG |
seen_mask |
Visibility/validity mask | 1024×768 8-bit grayscale PNG |
json |
Metadata dict with scene, camera, frame, and 3×3 intrinsics matrix |
JSON |
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