bike_shot11
dict | bike_shot6
dict | bike_shot8
dict | bike_shot9
dict | church_shot11
dict | church_shot14
dict | church_shot6
dict | electrical_grid_shot13
dict | electrical_grid_shot3
dict | equad_1_shot10
dict | equad_1_shot12
dict | equad_1_shot13
dict | equad_1_shot7
dict | equad_2_shot14
dict | equad_2_shot2
dict | equad_2_shot3
dict | equad_2_shot7
dict | equad_2_shot9
dict | equad_statue_shot12
dict | equad_statue_shot13
dict | fountain_shot14
dict | fountain_shot2
dict | fountain_shot5
dict | friend_1_shot11
dict | friend_1_shot15
dict | friend_1_shot2
dict | friend_1_shot3
dict | friend_2_shot2
dict | friend_2_shot5
dict | friend_2_shot7
dict | hoboken_shot5
dict | ias_walk_shot2
dict | ias_woods_shot2
dict | ias_woods_shot5
dict | lab_cars_shot1
dict | lab_debug_shot2
dict | lab_debug_shot4
dict | lab_drone_shot4
dict | lab_drone_shot5
dict | lab_drone_shot6
dict | lab_frisbee_shot2
dict | lab_misc_shot4
dict | lab_predator_shot1
dict | lab_puppet_shot1
dict | lab_teddy_shot1
dict | lake_shot14
dict | lake_shot7
dict | lake_shot8
dict | palmer_1_shot5
dict | palmer_1_shot8
dict | palmer_2_shot11
dict | palmer_2_shot18
dict | palmer_2_shot4
dict | palmer_2_shot7
dict | palmer_2_shot9
dict | path_shot10
dict | path_shot5
dict | path_shot8
dict | pulaski_skyway_shot7
dict | shrub_shot10
dict | shrub_shot15
dict | shrub_shot9
dict | statue_shot12
dict | statue_shot2
dict | statue_shot6
dict | statue_shot7
dict | street_shot10
dict | suburbs_shot3
dict | tower_shot1
dict | tower_shot10
dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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InFlux
We present Intrinsics in Flux (InFlux), a real-world benchmark that provides per-frame ground truth intrinsics annotations for videos with dynamic intrinsics. Compared to prior benchmarks, InFlux captures a wider range of intrinsic variations and scene diversity, featuring 143K+ annotated frames from 386 high-resolution indoor and outdoor videos with dynamic camera intrinsics.
For more information about this dataset, please visit our website: https://influx.cs.princeton.edu/. Please find data download and submission instructions in the official repository.
Viewing .mp4 Files
The .mp4 files in this dataset require VLC media viewer to play locally. Please download VLC here.
Dataset Structure
The dataset includes 386 .mp4 videos and 2 .json files in the root directory:
Dynamic Intrinsics Videos (
.mp4)
Videos with dynamic intrinsics, moving objects, and camera motion.Frame Counts and Split (
video_frame_count_and_split.json)
Maps each video to its number of frames and whether it belongs to the validation or test split.Validation Ground Truth (
gt_validation_dict.json)
Maps validation video frames to ground truth intrinsics values.
JSON File Structures
video_frame_count_and_split.json
{
"video_shot_1": {
"frame_count": 1234,
"split": "val"
}
}
frame_count– Number of frames in the video.split– Denotes which split the video belongs in. The value will either be "val" or "test".
gt_validation_dict.json
{
"video_shot_1": {
"0": {
"intrinsics_gt": {
"fx": ..., "fy": ..., "cx": ..., "cy": ...,
"k1": ..., "k2": ..., "p1": ..., "p2": ...
},
"intrinsics_gt_extrapolated": { ... },
"lens_metadata": {
"focal_length_mm": ...,
"focus_distance_m": ...
}
},
"1": { ... },
...
}
}
Per-frame keys:
intrinsics_gt– Ground truth intrinsics from look-up table (LUT) interpolation.fx, fy, cx, cy, k1, k2, p1, p2denote the intrinsics parameters as specified by the rad-tan Brown-Conrady distortion model.
intrinsics_gt_extrapolated– The same asintrinsics_gt, but also provides extrapolated intrinsics if lens metadata is outside of LUT bounds. It contains the same fields asintrinsics_gt.lens_metadata– Raw physical lens parametersfocal_length_mm,focus_distance_m
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