---
library_name: xcalib
tags:
- camera-lidar
- cross-modal-matching
- extrinsic-calibration
- jetson
- onnx
- tensorrt
pipeline_tag: keypoint-detection
---

# 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*.

<!-- public-only -->
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},
}
```
<!-- /public-only -->

## Documentation

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

- [Home & quickstart](https://radar-lab.github.io/xcalib/)
- [Input protocol](https://radar-lab.github.io/xcalib/protocol/)
- [Models & datasets - loading from the Hub](https://radar-lab.github.io/xcalib/hub/)
- [API reference](https://radar-lab.github.io/xcalib/api/)

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## 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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