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Datasets:
aengel-io
/
pointsdemo

Tags:
point-cloud
lidar
copc
indoor
navvis
panorama
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
version: int64
source: string
frame: string
positions: int64
cameras: list<item: string>
  child 0, item: string
intrinsics: struct<model: string, width: int64, height: int64, fx: int64, fy: int64, cx: double, cy: double>
  child 0, model: string
  child 1, width: int64
  child 2, height: int64
  child 3, fx: int64
  child 4, fy: int64
  child 5, cx: double
  child 6, cy: double
pose: string
levels: list<item: struct<width: int64, height: int64, file: string, format: string>>
  child 0, item: struct<width: int64, height: int64, file: string, format: string>
      child 0, width: int64
      child 1, height: int64
      child 2, file: string
      child 3, format: string
bin: struct<file: string, order: string, poses: struct<offset: int64, type: string, perImage: list<item:  (... 158 chars omitted)
  child 0, file: string
  child 1, order: string
  child 2, poses: struct<offset: int64, type: string, perImage: list<item: string>>
      child 0, offset: int64
      child 1, type: string
      child 2, perImage: list<item: string>
          child 0, item: string
  child 3, floor: struct<offset: int64, type: string, perPosition: string>
      child 0, offset: int64
      child 1, type: string
      child 2, perPosition: string
  child 4, sizes: list<item: struct<width: int64, offset: int64, type: string, note: string>>
      child 0, item: struct<width: int64, offset: int64, type: string, note: string>
          child 0, width: int64
          child 1, offset: int64
          child 2, type: string
          child 3, note: string
sky: struct<from: int64, step: int64, colors: list<item: null>>
  child 0, from: int64
  child 1, step: int64
  child 2, colors: list<item: null>
      child 0, item: null
home: string
photos: list<item: struct<name: string, position: list<item: double>, forward: list<item: double>>>
  child 0, item: struct<name: string, position: list<item: double>, forward: list<item: double>>
      child 0, name: string
      child 1, position: list<item: double>
          child 0, item: double
      child 2, forward: list<item: double>
          child 0, item: double
to
{'source': Value('string'), 'home': Value('string'), 'positions': List(List(Value('float64'))), 'photos': List({'name': Value('string'), 'position': List(Value('float64')), 'forward': List(Value('float64'))}), 'sky': {'from': Value('int64'), 'step': Value('int64'), 'colors': List(Value('null'))}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              version: int64
              source: string
              frame: string
              positions: int64
              cameras: list<item: string>
                child 0, item: string
              intrinsics: struct<model: string, width: int64, height: int64, fx: int64, fy: int64, cx: double, cy: double>
                child 0, model: string
                child 1, width: int64
                child 2, height: int64
                child 3, fx: int64
                child 4, fy: int64
                child 5, cx: double
                child 6, cy: double
              pose: string
              levels: list<item: struct<width: int64, height: int64, file: string, format: string>>
                child 0, item: struct<width: int64, height: int64, file: string, format: string>
                    child 0, width: int64
                    child 1, height: int64
                    child 2, file: string
                    child 3, format: string
              bin: struct<file: string, order: string, poses: struct<offset: int64, type: string, perImage: list<item:  (... 158 chars omitted)
                child 0, file: string
                child 1, order: string
                child 2, poses: struct<offset: int64, type: string, perImage: list<item: string>>
                    child 0, offset: int64
                    child 1, type: string
                    child 2, perImage: list<item: string>
                        child 0, item: string
                child 3, floor: struct<offset: int64, type: string, perPosition: string>
                    child 0, offset: int64
                    child 1, type: string
                    child 2, perPosition: string
                child 4, sizes: list<item: struct<width: int64, offset: int64, type: string, note: string>>
                    child 0, item: struct<width: int64, offset: int64, type: string, note: string>
                        child 0, width: int64
                        child 1, offset: int64
                        child 2, type: string
                        child 3, note: string
              sky: struct<from: int64, step: int64, colors: list<item: null>>
                child 0, from: int64
                child 1, step: int64
                child 2, colors: list<item: null>
                    child 0, item: null
              home: string
              photos: list<item: struct<name: string, position: list<item: double>, forward: list<item: double>>>
                child 0, item: struct<name: string, position: list<item: double>, forward: list<item: double>>
                    child 0, name: string
                    child 1, position: list<item: double>
                        child 0, item: double
                    child 2, forward: list<item: double>
                        child 0, item: double
              to
              {'source': Value('string'), 'home': Value('string'), 'positions': List(List(Value('float64'))), 'photos': List({'name': Value('string'), 'position': List(Value('float64')), 'forward': List(Value('float64'))}), 'sky': {'from': Value('int64'), 'step': Value('int64'), 'colors': List(Value('null'))}}
              because column names don't match

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LaMAR HGE, NavVis M6 point cloud and posed photos, for the web

One NavVis M6 session of the LaMAR dataset: the main building of ETH Zurich (HG, indoors), with RGB, and the photos of its six cameras at every capture position.

  • lamar-hge.copc.laz: the point cloud, 158,876,003 points, LAS 1.4 point format 7.
  • lamar-hge-photos/: the 3030 photos (505 capture positions x 6 cameras), with their poses, in a layout a browser can read by range requests:
    • photos.json describes the cameras (one PINHOLE model, 1442 x 1920), the levels and the layout of photos.bin.
    • photos.bin holds the poses (world_from_camera, in the point cloud's frame), the floor height under each position and the byte size of every image.
    • photos-720.ktx2pack and photos-1440.ktx2pack hold the images end to end, one KTX2 file each (BasisLZ / ETC1S, sRGB, mipmaps), at half and full resolution.

They serve a public web demo that streams both by HTTP range requests: https://lab.aengel.io/webgpu-pointcloud/

Source and credit

Sarlin, P.-E., Dusmanu, M., Schönberger, J. L., Speciale, P., Gruber, L., Larsson, V., Miksik, O., Pollefeys, M. (2022). LaMAR: Benchmarking Localization and Mapping for Augmented Reality. ECCV 2022. ETH Zurich and Microsoft. https://lamar.ethz.ch/

Original session: raw/HGE/sessions/navvis_2021-02-18_10.02.08.zip at https://cvg-data.inf.ethz.ch/lamar/, entries raw_data/pointcloud.ply, raw_data/images_undistr_center/, sensors.txt, trajectories.txt and images.txt.

Licence: CC BY-SA 4.0, the licence of the original data. This copy is shared under the same licence.

Changes from the original

Point cloud:

  • Converted from PLY to COPC (Cloud Optimized Point Cloud) with PDAL readers.ply and writers.copc, coordinates quantized to 1 mm (the original stores float32). Same coordinate frame (local, metres), nothing moved.
  • Kept: position and RGB. The 8-bit colours are stored as they are (0 to 255) in the 16-bit LAS colour fields.
  • Left out: the normals (nx, ny, nz) and curvature.
  • No point removed or added.

Photos:

  • Re-encoded from JPEG to KTX2 BasisLZ (ETC1S, quality 192) with mipmaps by KTX-Software ktx create; the half-resolution level is resampled to 720 x 960, the full one to 1440 x 1920 (sides rounded down to a multiple of 4 for GPU block formats; the whole image is kept, nothing cropped).
  • Poses copied from trajectories.txt as float32, unchanged.
  • Added: the floor height under each capture position, measured in the point cloud (the most common height within 30 cm of the cameras' centre).
  • The faces in the photos are blurred in the original; nothing else changed.
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