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Datasets:
hugsam
/
Room_Envelopes

Tasks:
Depth Estimation
Modalities:
Text
Size:
10K<n<100K
Tags:
3D
monocular
depth
License:
Dataset card Data Studio Files
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The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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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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