Add TruckDrive dataset support#22
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TruckDrive (Torc Robotics / Princeton, CVPR 2026) is a long-range highway
heavy-truck dataset. Add a first-class source-dataset processor + converter
covering cameras, the ego trajectory, the Aeva FMCW LiDAR, and ego-frame 3D
detections.
- src_datasets/truckdrive/: processor, converter, IO (extracted devkit
layout, gt_trajectory parsing, sync-id cross-sensor matching, float64x11
Aeva xyz decode) and calibration geometry (TF-tree BFS, pose assembly).
- enums.py: map each rig camera to the canonical CameraDirection that matches
its facing (FRONT, FRONT_LEFT, SIDE_LEFT, REAR_LEFT, ...) so keys stay
consistent across datasets, and add members only for the four genuinely-extra
views the eight canonical slots cannot name -- the forward telephoto pair
(FRONT_{LEFT,RIGHT}_NARROW) and the rear-facing side pair
(SIDE_{LEFT,RIGHT}_BACK). Additive, non-breaking.
- Detections are emitted in the ego (velodyne) frame with the ego vehicle's
own cab/trailer and DontCare groups excluded; the class taxonomy is
vendored from the paper's supplementary Table 2.
- configs/truckdrive.yaml, scripts/extract_truckdrive.sh and
prepare_dataset_truckdrive.sh, unit + env-gated real-frame tests, and a
README support-table row.
Radar, accumulated-depth, short-range Ouster LiDAR and lane lines have no
StandardE2E target yet and are not ingested.
Codecov Report❌ Patch coverage is 📢 Thoughts on this report? Let us know! |
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TruckDrive (Torc Robotics / Princeton, CVPR 2026) is a long-range highway heavy-truck dataset. Add a first-class source-dataset processor + converter covering cameras, the ego trajectory, the Aeva FMCW LiDAR, and ego-frame 3D detections.
Radar, accumulated-depth, short-range Ouster LiDAR and lane lines have no StandardE2E target yet and are not ingested.