LTtestMay10 — per-clip stride=1 30 fps test set
218 self-contained clips for pose / depth evaluation. Each clip is 41 frames at 30 fps stride=1 (1.37 s real time, 2.56 s when played at 16 fps), undistorted to a pinhole 832×480 model. All 6 surround cameras are included; DC depth is provided for the front camera only (matching the original LongtailTest).
Sourced from 126 KEEP UUIDs of NVIDIA's PhysicalAI-Autonomous-Vehicles dataset across chunks 234–237. The pipeline (download raw → ftheta undistort → DepthCrafter) is identical to the original LongtailTest, but applied at the native 30 fps cadence (no temporal downsampling).
Layout
chunk_234.tar # all kept clips for chunk 234 (~1.5–2 GB each)
chunk_235.tar
chunk_236.tar
chunk_237.tar
manifest_clips.jsonl # 218 entries {chunk, uuid, clip_id, window_start, displacement_m}
kept_clips.txt # 218 lines chunk\tuuid\tclip_id
dropped_clips.jsonl # 34 entries (displacement < 2 m within 1.37 s)
README.md
After extracting all four chunk tars:
chunk_NNN/<uuid>/clip_NNNNNN/
├── front.mp4 # 41 frames, 832×480, h264 CRF 18, 30 fps
├── cross_left.mp4 # same spec
├── cross_right.mp4
├── rear_left.mp4
├── rear_right.mp4
├── rear_tele.mp4
├── front_depth.pt # {'depth_sequence': (41, 1, 512, 832) fp16,
│ 'source_indices': [start..start+40]}
├── pose.pt # {'T_anchor_front': (11, 4, 4) world_from_cam in OpenCV,
│ first anchor = identity} (FRONT camera),
│ 'T_anchor_all': (6, 11, 4, 4) — same convention, one per view,
│ ordered by sensor_order; T_anchor_all[0] == T_anchor_front,
│ 'sensor_order': list of 6 camera names}
└── meta.pt # K (front, 3,3), K_all (6, 3, 3) target pinhole intrinsics,
E_rig_front (4,4), E_all (6, 4, 4) sensor extrinsics,
sensor_order [6 names], view_files [6 mp4 names],
frame_indices, timestamps_us, anchor_clip_idx,
anchor_src_idx, anchor_t_us, src_fps=30, stride=1,
window_start, chunk, uuid, clip_id, anchor_displacement_m
Per-UUID windows
Two 41-frame windows per UUID at 30 fps source frames:
| clip_id | window_start | end (exclusive) | real time start |
|---|---|---|---|
clip_000000 |
0 | 41 | 0.00 s |
clip_000001 |
300 | 341 | 10.00 s |
The two windows are 10 s apart in real time, providing diverse trajectories per UUID.
Pose anchors
T_anchor_front has 11 anchors at clip-frame indices 0, 4, 8, …, 40 (i.e. every 4th clip frame). At stride=1 from 30 fps, this equals real-time spacing ≈ 0.133 s, total span 1.37 s. Anchors are world_from_cam in OpenCV camera frame, with the first anchor forced to identity:
T_anchor_front[i] = inv(T_world_front[0]) @ T_world_front[i]
T_world_front(t) = T_world_rig(t) @ E_rig_front
T_world_rig(t) is interpolated from egomotion.offline.parquet via SLERP (rotation) + linear (translation) at the 11 anchor camera timestamps. E_rig_front comes from sensor_extrinsics.offline.parquet.
meta.K is the target pinhole intrinsics (constant across UUIDs):
fx = 400.0, fy = 411.0, cx = 415.0, cy = 338.0 # output 832×480
Motion filter
Clips with ‖T_anchor_front[10][:3,3] − T_anchor_front[0][:3,3]‖ < 2.0 m are excluded from the tarballs (not present at all). The dropped 34 clip ids are listed in dropped_clips.jsonl for transparency. 124 of the 126 UUIDs have at least one clip that passes the filter; 2 UUIDs (in chunk 236) have both clips dropped (parked / heavy traffic).
Counts
| chunk | UUIDs | clips total | kept (≥ 2 m) | dropped |
|---|---|---|---|---|
| 234 | 30 | 60 | (see manifest) | (see dropped_clips) |
| 235 | 35 | 70 | (see manifest) | |
| 236 | 34 | 68 | (see manifest) | |
| 237 | 27 | 54 | (see manifest) | |
| total | 126 | 252 | 218 | 34 |
Loading example
from huggingface_hub import snapshot_download
import torch, av
snapshot_download("luuuulinnnn/LTtestMay10", repo_type="dataset",
local_dir="LTtestMay10", allow_patterns=["chunk_234.tar","kept_clips.txt"])
# extract chunk tars locally, then:
clip_dir = "LTtestMay10/chunk_234/<uuid>/clip_000000"
pose = torch.load(f"{clip_dir}/pose.pt")["T_anchor_front"] # (11, 4, 4)
depth = torch.load(f"{clip_dir}/front_depth.pt")["depth_sequence"] # (41, 1, 512, 832) fp16
meta = torch.load(f"{clip_dir}/meta.pt") # K, E_rig_front, ...
container = av.open(f"{clip_dir}/front.mp4")
Provenance
- Raw video:
nvidia/PhysicalAI-Autonomous-Vehicleschunks 0234–0237 (camera_front_wide_120fov, 30 fps, 1920×1080 ftheta) - KEEP UUIDs: filter on Qwen2.5-VL-7B captions (sunny / bright_day / clear); 126 UUIDs across 4 chunks
- ftheta undistort: own reproduction of the original ray_wan pipeline (math verified pixel-equivalent within mpeg4 noise floor)
- DepthCrafter: tencent/DepthCrafter, max_res=1024, 5 inference steps, window_size=110, overlap=25, run on 30 fps undistorted videos
- Pose GT: SLERP+linear interpolation of
egomotion.offline.parquetat per-clip anchor timestamps; mean translation diff vs original LongtailTest GT is ~1.4 m on ~80 m trajectories (≈ 1.7 %, within egomotion sub-frame timestamp accuracy)