The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
HM3D Subset (3DVLM)
A small, fast-to-download slice of HM3D scenes, rendered into a uniform
posed-RGB-D format for quick model test-runs. This is a subset of the full
set: 100 scenes (randomly sampled, seed 0) out of 779, with all episodes
kept for each selected scene. For the complete data, see the full (private)
3dvlm-hm3d.
Contents
100 scenes, one .tar each under hm3d/. Each scene tar holds its episodes
(short 5-frame clips, 1–5 per scene → ~5–25 frames per scene). Each episode
extracts to its own directory:
00002-FxCkHAfgh7A_ep00/
├── images/ # frame_000000.jpg … frame_000004.jpg (5 RGB frames, 512×512)
├── depth.npy # (5, 512, 512) float32
├── valid_mask.npy # (5, 512, 512) bool — True where depth is valid
├── extrinsics.npy # (5, 4, 4) float32 — world→camera (w2c)
├── intrinsics.npy # (5, 3, 3) float32 — pinhole K
└── meta.json # scene_id, frame_ids (parallel to array index), image_size
An episode is a self-contained 5-frame clip with frame-to-frame overlap; treat
each as one short posed-RGB-D sequence. The first axis of every array is the frame,
matching meta.json's frame_ids and the sorted images/ files.
Conventions
- Coordinate frame: OpenCV (x-right, y-down, z-forward).
extrinsicsis the world→camera (w2c) matrix; invert it for camera→world. - Depth: z-depth in metres (distance along the camera z-axis, not Euclidean
ray length), returned natively by the simulator. Use
valid_maskfor valid pixels. - Intrinsics: fixed —
fx=fy=256,cx=cy=255.5(90° FOV, 512×512).
Quick start
import tarfile, json, numpy as np
from huggingface_hub import hf_hub_download
p = hf_hub_download("helioom/3dvlm-hm3d_subset", "hm3d/00002-FxCkHAfgh7A.tar", repo_type="dataset")
tarfile.open(p).extractall("hm3d/")
ep = "hm3d/00002-FxCkHAfgh7A_ep00"
meta = json.load(open(f"{ep}/meta.json"))
depth = np.load(f"{ep}/depth.npy") # (5, 512, 512)
K = np.load(f"{ep}/intrinsics.npy") # (5, 3, 3)
w2c = np.load(f"{ep}/extrinsics.npy") # (5, 4, 4)
License
Built on HM3D (Matterport), released for research use only under the Matterport end-user license; the same terms apply to this derived subset. See HM3D.
- Downloads last month
- -