Datasets:

mcocheteux
/

KITTI-C2L-Dataset

These historical training results use the frozen v1.0 x-only calibration convention. See the v1.1 calibration guide for the additive full-projection protocol and re-evaluation of these same checkpoints.

Download the verified checkpoints, source and evaluation harness (73 MB). The archive includes its licenses and checksums.

These results establish useful learning on the frozen data. They do not reproduce the published UniCal training recipe or advertised accuracy. All values below average seeds 42, 43 and 44; translation is MAE in centimetres and rotation is mean geodesic degrees. Lower is better.

Longer unchanged-main baseline

Model implementation: UniCal main commit 7c16fd81e4889d6f16076b1ec5b58835d876807b. Each C2L run completed 50 epochs, 925,350 encoder frames and 115,700 updates. Checkpoints were selected by sequence-07 validation loss at epochs 45/34/40 (one-based).

Held-out target protocol	Identity correction	C2L-trained UniCal	Usual-KITTI-trained UniCal
Native frozen C2L	6.6925 / 1.3287	4.0628 / 0.7746	7.2228 / 1.3284
Usual-relative control	5.0536 / 0.9542	3.5458 / 0.6061	5.6084 / 0.9543

C2L improves both metrics versus identity and the usual-KITTI comparator, overall and separately on each held-out drive. The usual comparator remains weak, so this is evidence that C2L is learnable, not evidence of matching a strong published baseline. Its runs used 61 epochs, 933,666 encoder frames and 116,754 updates (approximately 0.9% more exposure). Validation curves were non-monotonic; these are validation-selected checkpoints.

Recipe: 256×256 inputs, ImageNet RGB normalization, 8,192 spatial-loss points sampled after dense projection, BF16 mixed precision, batch 8, four spawned loader workers, pinned memory, uniform learning rate 3e-5 and one warmup epoch. The model uses pretrained MobileViT-small and 6D rotation outputs. This recipe is different from the paper's grayscale/RPY recipe and training duration.

Perturbation-policy comparison

Under the matched short recipe on the improved implementation, native errors were 4.2528 / 0.6148 for frozen C2L versus 4.4919 / 0.6524 for usual KITTI. On usual-relative targets they were 3.7726 / 0.5199 versus 3.2897 / 0.4555: 14.68% translation and 14.14% rotation deficits, both beyond the study's 10% margin, with the same finding on each test drive.

Regenerating usual-style online perturbations on C2L frames gave 3.2632 / 0.4368 on usual-relative targets and met the 10% comparison criterion on both protocols. These results support the frame/GT content and show that the frozen perturbation distribution affects transfer. They do not establish universal parity for frozen C2L.

Three-frame inference averaging improved frozen-C2L results from 4.3470 / 0.6343 to 4.1036 / 0.5944 on 2,604 common window anchors. Dedicated three-frame training did not establish an accuracy gain and used three times the encoder exposure. Keep those experiments distinct.

Full design, uncertainty and limitations: ASSESSMENT.md. Compact numerical evidence and original report hashes: assessment_results.json.

Reproducing the packaged baseline

The preparation script can create a separate local baseline package with three C2L-trained checkpoints. Exports contain plain CPU tensors, epoch and update counts, with no optimizer state or serialized Python objects. The manifest records the original checkpoint hashes and exported hashes.

The package includes a frozen UniCal source archive at main commit eebb3b0 (full hash in its manifest), with the same model/preprocessor as the training baseline and the independently usable loader fix. It also includes the experiment harness source at 2550c6d, its study protocol, and this release's offline-capable native evaluator. The verified archive is publicly downloadable through the link above.

After unpacking the baseline package, extract both unical-source.tar and harness-source.tar. Install the UniCal source with pip install /path/to/unical. For the pinned dependencies, use uv sync --project /path/to/unical --python 3.11 --locked and run with that environment's Python. Run the included evaluator:

python evaluate_baseline.py \
  --checkpoint main-c2l-long50-fast256-seed42/checkpoints/model.pt \
  --data-dir /path/to/kitti-c2l/data \
  --raw-root /path/to/kitti_raw \
  --device cpu --output seed42-native.json

Use --device cuda when available. Evaluate all three seeds for the reported mean. --max-samples 2 is a smoke check and is not a benchmark result. If the source is used without installation, set PYTHONPATH=/path/to/unical/src. Model construction disables pretrained downloads because the export contains all weights. The native evaluator uses stage 5, 256px and ImageNet RGB; it does not regenerate targets. The original experiment evaluator in the harness supports the secondary relative protocol and paired analyses. Its GPU path requires local CUDA; pass paths explicitly instead of its historical defaults.

The original evaluator used batches of two; the packaged evaluator keeps that default to minimize GPU-kernel rounding differences. Different devices, batch sizes and library versions can produce small numeric differences.

The training harness records Git provenance. Use Git checkouts at the recorded revisions, or initialize and commit each extracted unical and harness source directory locally before new training. Snapshot commits will have new IDs; the source-archive hashes preserve their original provenance.

For new training, install the source archive and use the included harness with that source on PYTHONPATH:

PYTHONPATH=/path/to/unical/src python harness/experiments/run_comparison.py \
  --variant main --protocol frames --stage 5 --seed 42 \
  --data-dir /path/to/kitti-c2l/data --raw-root /path/to/kitti_raw \
  --epochs 50 --warmup-epochs 1 --batch-size 8 --precision bf16-mixed \
  --image-size 256 --max-loss-points 8192 --num-workers 4 --pin-memory \
  --validation-period 1 \
  --output /path/to/new-run

Use seeds 43/44 for repeats. The harness installs no dataset and performs no upload. A basic train.py data=kitti_c2l command uses different defaults and must not be presented as exact reproduction of these numbers.

Licensing

Dataset metadata is CC BY-NC-SA 3.0. UniCal code and the separately packaged model artifacts are supplied under CC BY-NC 4.0, with attribution and source license included; these files are not relicensed as MIT or as dataset metadata. The backbone originates from apple/mobilevit-small, whose model card links to the Apple sample code license. That upstream notice is retained separately and is not replaced by CC BY-NC 4.0; see THIRD_PARTY.md. No training images or point clouds are bundled with either package. Public release of the baseline package is a separate publication step.