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xcalib

Keypoint Detection
ONNX
TensorRT
xcalib
camera-lidar
cross-modal-matching
extrinsic-calibration
jetson
Model card Files
xet
 Community
How to use from the
Use from the
TensorRT library
# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

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 

from xcalib import Matcher

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

Citation 

@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
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