Datasets:

mcocheteux
/

KITTI-C2L-Dataset

# KITTI-C2L v1 evaluation protocol

Use the exact files and hashes in `release/v1/manifest.json`. For the recommended
benchmark, remove sequence 07 from the source training file and use it only for
validation. Test on sequences 09 and 10. All five stages of a frame, and every
window containing it, belong to the same partition.

## Targets

Native evaluation uses the frozen sensor perturbations in the Parquet files.
Construct `T_gt` and `T_init` in the same unit system. The UniCal adapter target
is `D = T_init @ inverse(T_gt)`; it predicts decalibration, so the correction
applied to the input extrinsic is its inverse. Do not regress the raw sampled
camera/LiDAR translations as if either one were the composed relative target.

The secondary usual-relative control retains the source frames and GT but
replaces the input error by one camera-relative transform drawn uniformly at
±1 degree and ±10 cm per axis, so `T_init = D_relative @ T_gt`. It is not the
frozen native dataset. Our existing control implementation uses UniCal's
`ErrorGenerator` and, for validation/test, a Torch generator seeded with
`sample_seed % 2**63` (relative_seed zero). The exact sampler and rotation
conversion live in the pinned UniCal evaluation source; substituting NumPy
sampling will not reproduce those targets. Online training draws vary; the
aligned experiment changes both training and validation perturbation policies.
## Metrics
For N targets `D_i = (R_i, t_i)` and predictions `(Rhat_i, that_i)`, report:

- Translation MAE: `sum_i sum_j abs(that_ij - t_ij) / (3*N)`, in centimetres.
  Multiply metre-based errors by 100. This is not Euclidean translation norm.
- Rotation: mean geodesic SO(3) angle between `Rhat_i` and `R_i`, in degrees.
  For valid rotations it is `acos(clip((trace(Rhat_i.T @ R_i)-1)/2,-1,1))`.
  Our experiment evaluator uses SciPy Rotation normalization for small numerical
  deviations from orthogonality. Euler-angle MAE is a different metric and
  cannot be compared directly with these numbers.

Include identity correction, per-drive results and the aggregate. Report
sample counts, seeds, selected checkpoint epochs, image size, point sampling,
normalization, precision, training updates and encoder-frame exposure. Select
checkpoints on validation, never on the test values reported here.

The existing paired comparisons resample training seeds and shared temporal
blocks of 30 anchors, using 5,000 bootstrap draws and 95% intervals. Their
10% noninferiority screen is a study decision rule, not a universal definition
of dataset validity. Dependence remains within this single KITTI rig and the
two held-out drives; intervals do not establish generalization to other rigs.

## Temporal methods

Use the windows configuration when assuming a fixed error over consecutive
frames. Reset state at each sequence, stage and window boundary. Final windows
can be shorter than 30 frames. Report the minimum context and anchor-selection
policy. Our three-frame comparison uses 2,604 common anchors, versus 2,792 for
single-frame evaluation. Compare methods on identical anchors; a smaller
anchor set changes the benchmark. Disclose whether fusion is inference-only
or trained, and match encoder exposure when claiming training efficiency.

## Geometry version

The frozen v1 camera-2 calibration uses rectification plus the x-only stereo
baseline shift. The full `K^-1 @ P[:,3]` baseline differs by up to about 0.62 cm
on these dates. The source audit validates agreement with the frozen producer
convention, not equivalence to every alternative calibration construction.
Keep v1 unchanged so results remain comparable. Future geometry changes need
new hashes, a new version and updated baselines.

	# KITTI-C2L v1 evaluation protocol

	Use the exact files and hashes in `release/v1/manifest.json`. For the recommended
	benchmark, remove sequence 07 from the source training file and use it only for
	validation. Test on sequences 09 and 10. All five stages of a frame, and every
	window containing it, belong to the same partition.

	## Targets

	Native evaluation uses the frozen sensor perturbations in the Parquet files.
	Construct `T_gt` and `T_init` in the same unit system. The UniCal adapter target
	is `D = T_init @ inverse(T_gt)`; it predicts decalibration, so the correction
	applied to the input extrinsic is its inverse. Do not regress the raw sampled
	camera/LiDAR translations as if either one were the composed relative target.

	The secondary usual-relative control retains the source frames and GT but
	replaces the input error by one camera-relative transform drawn uniformly at
	±1 degree and ±10 cm per axis, so `T_init = D_relative @ T_gt`. It is not the
	frozen native dataset. Our existing control implementation uses UniCal's
	`ErrorGenerator` and, for validation/test, a Torch generator seeded with
	`sample_seed % 2**63` (relative_seed zero). The exact sampler and rotation
	conversion live in the pinned UniCal evaluation source; substituting NumPy
	sampling will not reproduce those targets. Online training draws vary; the
	aligned experiment changes both training and validation perturbation policies.

	## Metrics

	For N targets `D_i = (R_i, t_i)` and predictions `(Rhat_i, that_i)`, report:

	- Translation MAE: `sum_i sum_j abs(that_ij - t_ij) / (3*N)`, in centimetres.
	Multiply metre-based errors by 100. This is not Euclidean translation norm.
	- Rotation: mean geodesic SO(3) angle between `Rhat_i` and `R_i`, in degrees.
	For valid rotations it is `acos(clip((trace(Rhat_i.T @ R_i)-1)/2,-1,1))`.
	Our experiment evaluator uses SciPy Rotation normalization for small numerical
	deviations from orthogonality. Euler-angle MAE is a different metric and
	cannot be compared directly with these numbers.

	Include identity correction, per-drive results and the aggregate. Report
	sample counts, seeds, selected checkpoint epochs, image size, point sampling,
	normalization, precision, training updates and encoder-frame exposure. Select
	checkpoints on validation, never on the test values reported here.

	The existing paired comparisons resample training seeds and shared temporal
	blocks of 30 anchors, using 5,000 bootstrap draws and 95% intervals. Their
	10% noninferiority screen is a study decision rule, not a universal definition
	of dataset validity. Dependence remains within this single KITTI rig and the
	two held-out drives; intervals do not establish generalization to other rigs.

	## Temporal methods

	Use the windows configuration when assuming a fixed error over consecutive
	frames. Reset state at each sequence, stage and window boundary. Final windows
	can be shorter than 30 frames. Report the minimum context and anchor-selection
	policy. Our three-frame comparison uses 2,604 common anchors, versus 2,792 for
	single-frame evaluation. Compare methods on identical anchors; a smaller
	anchor set changes the benchmark. Disclose whether fusion is inference-only
	or trained, and match encoder exposure when claiming training efficiency.

	## Geometry version

	The frozen v1 camera-2 calibration uses rectification plus the x-only stereo
	baseline shift. The full `K^-1 @ P[:,3]` baseline differs by up to about 0.62 cm
	on these dates. The source audit validates agreement with the frozen producer
	convention, not equivalence to every alternative calibration construction.
	Keep v1 unchanged so results remain comparable. Future geometry changes need
	new hashes, a new version and updated baselines.