PIVOT: A Multi-Trajectory Dataset and Testbed for Pose, Intrinsics, and Novel Viewpoint Evaluation in Real-World 3D Reconstruction
Paper • 2608.25401 • Published
Dataset Viewer
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
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: (... 1209 chars omitted)
child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 1197 chars omitted)
child 0, _id: struct<$oid: string>
child 0, $oid: string
child 1, filepath: string
child 2, tags: list<item: null>
child 0, item: null
child 3, _media_type: string
child 4, _rand: double
child 5, scene: string
child 6, trajectory: string
child 7, resolution: list<item: int64>
child 0, item: int64
child 8, duration_s: double
child 9, message_count: int64
child 10, channel_count: int64
child 11, topics: list<item: string>
child 0, item: string
child 12, schemas: list<item: string>
child 0, item: string
child 13, has_image: bool
child 14, has_pointcloud: bool
child 15, has_colmap_pose: bool
child 16, n_points_kept: int64
child 17, n_points_dropped: int64
child 18, timestamps_are_synthetic: bool
child 19, mandatory_level: string
child 20, motion_type: string
child 21, altitude_band: string
child 22, camera_direction: string
child 23, lens_type: string
child 24, capture_mode: string
child 25, capture_device: string
child 26, aspect_ratio: string
child 27, fov_h: double
child 28, fov_v: do
...
string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item: (... 306 chars omitted)
child 0, name: string
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child 2, embedded_doc_type: string
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child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
child 0, name: string
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child 2, embedded_doc_type: null
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child 0, item: null
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child 9, created_at: struct<$date: string>
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workspaces: list<item: null>
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tags: list<item: null>
child 0, item: null
persistent: bool
slug: string
created_at: struct<$date: string>
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child 0, item: null
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 343, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: (... 1209 chars omitted)
child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 1197 chars omitted)
child 0, _id: struct<$oid: string>
child 0, $oid: string
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child 2, tags: list<item: null>
child 0, item: null
child 3, _media_type: string
child 4, _rand: double
child 5, scene: string
child 6, trajectory: string
child 7, resolution: list<item: int64>
child 0, item: int64
child 8, duration_s: double
child 9, message_count: int64
child 10, channel_count: int64
child 11, topics: list<item: string>
child 0, item: string
child 12, schemas: list<item: string>
child 0, item: string
child 13, has_image: bool
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child 15, has_colmap_pose: bool
child 16, n_points_kept: int64
child 17, n_points_dropped: int64
child 18, timestamps_are_synthetic: bool
child 19, mandatory_level: string
child 20, motion_type: string
child 21, altitude_band: string
child 22, camera_direction: string
child 23, lens_type: string
child 24, capture_mode: string
child 25, capture_device: string
child 26, aspect_ratio: string
child 27, fov_h: double
child 28, fov_v: do
...
string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item: (... 306 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: string
child 3, subfield: string
child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: null
child 3, subfield: null
child 4, fields: list<item: null>
child 0, item: null
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
workspaces: list<item: null>
child 0, item: null
tags: list<item: null>
child 0, item: null
persistent: bool
slug: string
created_at: struct<$date: string>
child 0, $date: string
last_deletion_at: null
saved_views: list<item: null>
child 0, item: null
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for PIVOT-mcap
This is a FiftyOne dataset with 103 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/PIVOT-mcap")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset Description
PIVOT-mcap re-encodes the PIVOT dataset (Raymond, 2026) — a five-scene, multi-trajectory drone photogrammetry dataset for evaluating NeRF and 3D Gaussian Splatting under realistic capture conditions — as 103 MCAP episodes, one per drone trajectory. Each episode pairs the trajectory's RGB frame stream with PIVOT's dual pose representation (sensor-derived measured pose and COLMAP-optimized pose per frame), dual camera calibration (physical/offline calibration and COLMAP-optimized intrinsics), per-frame pose error, and the scene's shared sparse point cloud, so that a trajectory can be scrubbed through as a synchronized 3D episode in FiftyOne/Foxglove instead of browsed as a folder of independently posed frames.
- Curated by: Mary Raymond (original PIVOT dataset, capture, and processing pipeline); Harpreet Sahota (this MCAP re-encoding and FiftyOne dataset)
- Funded by: [More Information Needed]
- Shared by: Harpreet Sahota (this derived MCAP dataset); Mary Raymond (original PIVOT dataset, shared as
MaryRaymond/PIVOTon the Hugging Face Hub) - Language(s): en (dataset card only — the underlying data is imagery, camera poses, and a point cloud, with no natural-language content)
- License: CC BY-NC 4.0. The source PIVOT dataset is released under CC BY-NC 4.0 (non-commercial); the PIVOT toolchain source code is released separately under the MIT License.
Dataset Sources
- Repository: MaryRaymond/PIVOT (source dataset); maryraymond/PIVOT (processing/visualization/benchmark toolchain)
- Paper: Mary Raymond, "PIVOT: A Multi-Trajectory Dataset and Testbed for Pose, Intrinsics, and Novel Viewpoint Evaluation in Real-World 3D Reconstruction," arXiv:2608.25401
- Demo: [More Information Needed]
Uses
Direct Use
- Exploring and visualizing PIVOT's dual-pose, dual-intrinsic drone trajectories in 3D: scrubbing through a camera moving along its measured GPS/IMU/gimbal path and, where COLMAP registered the frame, its optimized pose, against the scene's shared sparse point cloud.
- Prototyping or debugging FiftyOne/Foxglove multimodal (MCAP) pipelines against a real, non-synthetic example of "one moving camera against one static map with more than one competing pose source."
- Non-commercial research uses consistent with the source dataset's benchmark design: studying novel-view trajectory generalization (seen vs. unseen trajectories), pose-source sensitivity (measured vs. COLMAP-optimized poses), and intrinsic-source sensitivity (calibrated vs. COLMAP-optimized intrinsics), as defined in the PIVOT paper's three benchmark families.
Out-of-Scope Use
- Commercial use: the dataset inherits PIVOT's CC BY-NC 4.0 license and is non-commercial only.
- Anything relying on real elapsed flight time or frame-to-frame timing: MCAP
log_timevalues in every episode are a synthetic 10 fps sequence clock (timestamps_are_synthetic=Trueon every sample), not the drone's real capture timestamps, since PIVOT ships no real per-frame timestamps in the source data. - Broad claims about camera platforms, environments, or motion diversity beyond this release: PIVOT v1 (and this re-encoding) covers five scenes captured with a single DJI Mini 4 Pro drone, as noted in the paper's own limitations discussion.
Dataset Structure
This is a FiftyOne dataset with media_type == "multimodal" and 103 samples — one sample per (scene, trajectory) pair across PIVOT's five scenes (church: 24, backyard: 22, frontyard: 22, victorian_garden: 18, village_street: 17). No splits or tags are defined; PIVOT leaves train/eval trajectory selection as a downstream, benchmark-specific choice rather than a fixed property of the data. Each sample's filepath points to one .mcap file (an "episode") containing that trajectory's full frame sequence, both pose streams, camera calibration, and the scene's point cloud.
Sample fields
| Field | FiftyOne type | Description |
|---|---|---|
filepath |
StringField |
Path to the episode's .mcap file |
scene |
StringField |
Scene name (5 values, verbatim from PIVOT) |
trajectory |
StringField |
Trajectory name within the scene (verbatim from PIVOT) |
resolution |
ListField |
[width, height] pixel resolution of the trajectory's frames |
duration_s |
FloatField |
Episode duration under the synthetic 10 fps clock (derived) |
message_count |
IntField |
Total MCAP messages in the episode (derived, from the .mcap file) |
channel_count |
IntField |
Total MCAP channels/topics in the episode (derived) |
topics |
ListField |
MCAP topic names present in the episode (derived) |
schemas |
ListField |
Foxglove/JSON schema names present in the episode (derived) |
has_image |
BooleanField |
Whether a foxglove.CompressedImage channel is present (derived; always True) |
has_pointcloud |
BooleanField |
Whether a foxglove.PointCloud channel is present (derived; always True) |
has_colmap_pose |
BooleanField |
Whether any frame in the episode has a registered COLMAP pose (derived; False only for church/rocket_upward) |
n_points_kept |
IntField |
Point-cloud points retained after outlier trimming (derived) |
n_points_dropped |
IntField |
Point-cloud points dropped as outliers, farther than 15 m from the point cloud's centroid (derived) |
timestamps_are_synthetic |
BooleanField |
Always True; flags that MCAP log_time is a synthetic clock, not real capture time |
mandatory_level |
StringField |
core or optional, per PIVOT's trajectory taxonomy (verbatim) |
motion_type |
StringField |
e.g. orbit, traversal, bev_orbit, bev_traverse, traverse_loop, vertical_ascent, scattered, panorama_360 (verbatim) |
altitude_band |
StringField |
low / mid / high (verbatim) |
camera_direction |
StringField |
e.g. scene_inward, along_track, nadir (verbatim) |
lens_type |
StringField |
standard_fov or wide_fov (verbatim) |
capture_mode |
StringField |
video_frames or photos (verbatim) |
capture_device |
StringField |
dji_drone_mini_4_pro (verbatim) |
aspect_ratio |
StringField |
16:9 or 4:3 (verbatim) |
fov_h, fov_v, fov_diag |
FloatField |
Trajectory field of view in degrees (verbatim; 0 on some zero-COLMAP-registration trajectories, per PIVOT's own export) |
average_rot_error |
FloatField |
Trajectory-mean rotation error between measured and COLMAP poses, degrees (verbatim) |
average_cam_center_error_distance |
FloatField |
Trajectory-mean camera-center distance error, meters (verbatim) |
missing_colmap_frames |
IntField |
Count of frames without a registered COLMAP pose (verbatim) |
number_frames_in_traj |
IntField |
Total frame count in the trajectory (verbatim) |
scene_total_frames_number, scene_colmap_reg_frames_number, scene_pointcloud_number, scene_observations, scene_scene_diameter, scene_aabb_diagonal, scene_max_rotation_angle, scene_mean_track_length, scene_colmap_per_image_observation, scene_mean_observations_per_image, scene_mean_reprojection_error_px |
IntField / FloatField |
Scene-level reconstruction statistics, copied verbatim from PIVOT's scene_data.json onto every sample belonging to that scene |
Label types and why
This dataset carries no per-sample FiftyOne label field (no Classification, Detections, etc.). PIVOT's actual content — a moving camera with two competing pose sources against one static map — is a synchronized multi-topic time series, not a set of independent per-image annotations, so it is encoded as MCAP topics inside each sample's file instead:
/camera_measured/image_raw(foxglove.CompressedImage) — one JPEG per frame/camera_measured/calibration(foxglove.CameraCalibration) — physical camera intrinsics, paired to the image channel (viamcap.calibration_topicmetadata) so camera frustums render in the viewer/camera_colmap/calibration(foxglove.CameraCalibration) — COLMAP-optimized intrinsics; frustum geometry only, not paired to an image channel, since only one calibration topic can pair with a given image topic/tf(foxglove.FrameTransform) —world -> camera_measuredon every frame;world -> camera_colmaponly on frames with a registered COLMAP pose/map/sparse_pointcloud(foxglove.PointCloud) — the scene's sparse reconstruction, logged once at the start of the episode, world frame/pose_error(custom JSON schemapivot_pose_error) —rot_error_deg,camera_center_error_distance_m, only on frames with a registered COLMAP pose/pose_error/line(foxglove.SceneUpdate,LinePrimitive) — a visual line segment connecting the measured and COLMAP camera centers, only on frames with a registered COLMAP pose
dataset.info
Empty. All dataset-level facts (scene/trajectory taxonomy, pose-error statistics, reconstruction statistics) are attached as per-sample scalar fields (see the field table above) rather than stored on dataset.info.
Parsing decisions
- Coordinate systems converted. PIVOT stores poses as 4x4 camera-to-world matrices in a NED world frame (X-north, Y-east, Z-down) with an OpenGL-style camera convention (verified against PIVOT's own
dji_drone_mini_4.pyandviser_visualization.py). These are rotated into a Z-up world frame and an OpenCV/ROS optical camera convention for Foxglove/FiftyOne rendering — applied identically to camera poses and to point-cloud vertices, since both live in the same source frame. - Timestamps are synthetic. PIVOT ships no real per-frame capture timestamps (JPEG EXIF
DateTimeOriginal/CreateDateare stripped; only GPS and gimbal angles survive). MCAPlog_timeuses a synthetic 100 ms/frame (10 fps) sequence clock to preserve frame order without claiming a real elapsed flight duration. - Missing COLMAP poses are never fabricated. Frames without a registered COLMAP pose (
colmap_pose_c2wabsent from the source JSON, notnull) get noworld -> camera_colmaptransform and no/pose_errormessage for that frame.church/rocket_upwardhas zero registered frames across its entire trajectory — a documented issue in PIVOT's own dataset card, confirmed independently, not introduced by this re-encoding. - Distortion model picked from available keys, not from the
camera_typelabel. Standard-FOV trajectories use OpenCVplumb_bob(k1, k2, p1, p2, k3). Wide-FOV trajectories are taggedOPENCV_FISHEYEin PIVOT, but only COLMAP's own per-scene intrinsics carry a genuine 4-coefficient (k1-k4) fisheye fit; the measured/calibration-file version of the same physical lens hasp1 = p2 = 0and is missingk4in PIVOT'sscene_data.jsonexport (an upstream data-loss bug). The correctk4was recovered from PIVOT's own raw per-camera calibration files and cross-checked against the other six shared intrinsic values before substitution. - Fisheye monotonicity guard. PIVOT's
k1-k4fisheye fits are high-order (theta^8) polynomials that can become non-invertible within the lens' own field of view. When that happens within a trajectory's own computed half-diagonal FOV,k4is dropped to0(leaving ak1-k3fit) rather than shipping a calibration that a fisheye rectifier cannot invert; this is logged whenever it occurs. - Point-cloud outlier trimming. Sparse-reconstruction points farther than 15 m from the point cloud's centroid are dropped before logging (
n_points_kept/n_points_droppedrecord the counts per sample). - In-pipeline geometric check. For every episode, the median distance from measured camera centers to the point cloud's centroid is asserted to be smaller than the scene's own
aabb_diagonal, as a sanity check on the coordinate conversion (median, not mean, to stay robust to isolated GPS glitches without silently dropping frames). - Scene- and trajectory-level statistics are copied verbatim from PIVOT's own
scene_data.json, not recomputed. - Raw COLMAP intermediates were never read.
PYCOLMAP_soft_prior/(roughly 26 GB of feature-matching database and candidate sparse models per scene) was excluded from the source download and never touched during MCAP authoring; only PIVOT's curatedscene_data.jsonandsparse_model.plyper scene were used, since PIVOT itself already summarizes the winning reconstruction into those two files.
Dataset Creation
Curation Rationale
PIVOT was designed to separate several favorable assumptions that are typically bundled together in NeRF/3DGS benchmarks (offline-optimized poses, per-scene-optimized intrinsics, reconstruction-friendly trajectories, held-out views drawn from the same trajectory family used in training) by making trajectory identity, pose source, and intrinsic source independently selectable. This MCAP/FiftyOne re-encoding preserves that dual-pose, dual-intrinsic design while making each trajectory explorable as a single synchronized 3D episode, rather than a folder of independently posed frames with metadata attached.
Source Data
Data Collection and Processing
PIVOT v1 covers five real-world scenes (Church, Village Street, Victorian Garden, Frontyard, Backyard), each captured with a single DJI Mini 4 Pro drone flown along 17-24 named trajectories covering reconstruction-friendly orbits, robot-like traversals, and extrapolation-oriented motion (bird's-eye-view sweeps, vertical ascent, scattered viewpoints, 360-degree panoramas). PIVOT's own raw-data pipeline (as described in the paper): read trajectory metadata and raw captures, sample video frames using translation/rotation thresholds (or use captured stills directly), extract EXIF/XMP metadata, compute measured poses from device position/orientation metadata, transform poses into the NED world frame and OpenGL camera convention, run COLMAP feature extraction and matching with measured positions injected as soft position priors, select the best reconstruction, retain optimized poses and intrinsics alongside measured poses and calibrated intrinsics, compute per-frame pose errors and trajectory/scene statistics, and compute a directed pose-Chamfer trajectory-distance matrix.
For this MCAP/FiftyOne re-encoding specifically: only PIVOT's already-curated scene_data.json (poses, calibration, taxonomy, statistics) and sparse_model.ply (the winning sparse point cloud) per scene were read. One .mcap episode was authored per (scene, trajectory) pair (103 total) using foxglove-sdk, applying the coordinate conversions and edge-case handling listed under Parsing decisions above, then loaded into FiftyOne as one fo.Sample per episode.
Who are the source data producers?
Mary Raymond (independent researcher), who designed and executed the PIVOT capture, processing, and benchmark pipeline, including piloting the DJI Mini 4 Pro drone. Per the paper's acknowledgements, drone captures took place at a site made accessible with support from Clare County Council and Bunratty Castle & Folk Park.
Annotations
Annotation process
There is no separate human annotation process. Per-frame pose, pose-error, and calibration values are produced entirely by PIVOT's capture and processing pipeline: measured poses come from the drone's onboard GPS, flight attitude, and gimbal attitude sensors; COLMAP-optimized poses and per-scene intrinsics come from PYCOLMAP structure-from-motion using the measured positions as soft priors; physical/offline camera calibration comes from a checkerboard calibration procedure documented in PIVOT's own toolchain.
Who are the annotators?
Not applicable — no human annotators. All pose and calibration values are either sensor-derived or algorithmically computed (COLMAP structure-from-motion), as described above.
Personal and Sensitive Information
[More Information Needed]. The scenes are outdoor drone captures at a real, named site (Bunratty Castle & Folk Park, per the paper's acknowledgements) and could incidentally contain identifiable people, vehicles, or property; neither the paper nor the toolchain describes an anonymization process.
Citation
BibTeX:
@article{raymond2026pivot,
title = {PIVOT: A Multi-Trajectory Dataset and Testbed for Pose, Intrinsics, and Novel Viewpoint Evaluation in Real-World 3D Reconstruction},
author = {Raymond, Mary},
journal = {arXiv preprint arXiv:2608.25401},
year = {2026}
}
APA:
Raymond, M. (2026). PIVOT: A multi-trajectory dataset and testbed for pose, intrinsics, and novel viewpoint evaluation in real-world 3D reconstruction. arXiv preprint arXiv:2608.25401.
More Information
- Source dataset: MaryRaymond/PIVOT
- PIVOT toolchain (processing, visualization, export, benchmarking): github.com/maryraymond/PIVOT
- Known issue carried over from the source dataset:
church/rocket_upwardhas no COLMAP-registered frames, documented in the source dataset's own card. This re-encoding preserves that by omitting theworld -> camera_colmaptransform and/pose_errortopic entirely for that episode, rather than fabricating data. - This is a derived, third-party re-encoding of PIVOT into MCAP/FiftyOne format, not an official PIVOT release. The source dataset and paper are the authoritative reference for PIVOT itself.
Dataset Card Authors
Harpreet Sahota (MCAP re-encoding and FiftyOne dataset card)
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