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UArizona/
xcalib

xcalib

camera-lidar

cross-modal-matching

extrinsic-calibration

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xcalib model weights

Trained checkpoints for the matching front-end studied in Position Encoding in Detection-Based LiDAR-Camera Matching: A Diagnostic Study at Infrastructure Sites.

This repo is public and accompanies the accepted xcalib paper.

Authors

Lihao Guo, Jiahao Tang, Tam Bang, Tianya Zhang, Austin Harris, Mina Sartipi, Siyang Cao

Usage

python

from xcalib import Matcher

matcher = Matcher.from_pretrained("crlite", site="a9_dataset_r02_s01")

Citation

bibtex

@article{guo2026xcalib,
  author  = {Guo, Lihao and Tang, Jiahao and Bang, Tam and Zhang, Tianya and
             Harris, Austin and Sartipi, Mina and Cao, Siyang},
  title   = {Position Encoding in Detection-Based LiDAR--Camera Matching:
             A Diagnostic Study at Infrastructure Sites},
  journal = {IEEE Sensors Letters},
  year    = {2026},
  note    = {Accepted. Paper URL pending. Code:
             https://github.com/radar-lab/xcalib},
}

Documentation

Documentation (installation, API reference, input protocol, ONNX/TensorRT export) is hosted at https://radar-lab.github.io/xcalib:

Files

model	site	file	sha256
`calibrefine`	`a9_dataset_r02_s01`	`checkpoints/calibrefine_a9_dataset_r02_s01_best.pth`	`768cce41b221c64136060e26c9573f04124c839a47397aef30de6a1e9152efe1`
`calibrefine`	`a9_dataset_r02_s01`	`configs/calibrefine_a9_dataset_r02_s01.yaml`	`8bccf8eaf479e5a238921c2b450a9990d8edab33ba08b645960379991e1a1feb`
`crlite`	`a9_dataset_r02_s01`	`checkpoints/crlite_a9_dataset_r02_s01_best.pth`	`16a07ed95befb632c67fe48ac3b62bdb342e21ec26072649b1acb556e0bd5dd8`
`crlite`	`a9_dataset_r02_s01`	`configs/crlite_a9_dataset_r02_s01.yaml`	`0a1cc325c6cc7925cb8304a4500accd68bfa0474b13e9f9f318dbb9f2281ff46`
`crlite_2dpe`	`a9_dataset_r02_s01`	`checkpoints/crlite_2dpe_a9_dataset_r02_s01_best.pth`	`c0c523d0fc945432fb9ef03132774bed2508ade91512c4a699b2d8201103a920`
`crlite_2dpe`	`a9_dataset_r02_s01`	`configs/crlite_2dpe_a9_dataset_r02_s01.yaml`	`b016e476b1a146ed8098bb9f957d9b88796005f55e328a15ac2b53b42d6ecd1d`
`crlite_vit_exp1`	`a9_dataset_r02_s01`	`checkpoints/crlite_vit_exp1_a9_dataset_r02_s01_best.pth`	`66d0cd7a7c1d88d8415da844798ea978d415c184409989b9a2f13a0061c57222`
`crlite_vit_exp1`	`a9_dataset_r02_s01`	`configs/crlite_vit_exp1_a9_dataset_r02_s01.yaml`	`12df9d18d3e977954ffd7aaa92be5bb29d5c7ee374a136c026b32362946492ce`
`crlite_vit_exp3`	`a9_dataset_r02_s01`	`checkpoints/crlite_vit_exp3_a9_dataset_r02_s01_best.pth`	`faa7cc851cb11ac748657edf4bfb03d9988d108e936c3ba46950ec734d48358d`
`crlite_vit_exp3`	`a9_dataset_r02_s01`	`configs/crlite_vit_exp3_a9_dataset_r02_s01.yaml`	`62d320c6f2451dd4669cacaf007013ab206c8f91680441aa7f37d4f065b57acf`

UArizona/xcalib