loftr 0.1.1

Native Rust/tch implementation of LoFTR feature matching
Documentation 
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# `loftr`

Native Rust/tch LoFTR feature matching.

## What This Crate Includes

- LoFTR config presets
- native model construction in Rust with `tch`
- loading Kornia-compatible state dict weights
- pairwise feature matching
- optional debug-stage dumps for validation

## Public API

- `LoftrConfig` for preset and custom model configuration
- `LoftrModel` for model construction, weight loading, and inference
- `LoftrMatches` for match outputs
- `LoftrDebugStages` for validation-oriented stage summaries
- `normalize_loftr_image` for converting supported image layouts into LoFTR input tensors
- `LoftrError` for public error handling

## Upstream References

- [LoFTR upstream](https://github.com/zju3dv/LoFTR)
- [Kornia upstream](https://github.com/kornia/kornia)
- [kornia-rs upstream](https://github.com/kornia/kornia-rs)

## Weights

This crate does not ship pretrained weights.

Generate them locally from the workspace root:

```bash
./scripts/generate_loftr_state_dict.sh
```

Then load them from:

```text
artifacts/weights/loftr_outdoor_state_dict.safetensors
```

## Example

```rust,no_run
use loftr::{LoftrConfig, LoftrModel};
use tch::{Device, Kind, Tensor};

let mut model = LoftrModel::new(Device::Cpu, LoftrConfig::outdoor())?;
model.load_weights("artifacts/weights/loftr_outdoor_state_dict.safetensors")?;

let image0 = Tensor::rand([1, 1, 128, 128], (Kind::Float, Device::Cpu));
let image1 = Tensor::rand([1, 1, 128, 128], (Kind::Float, Device::Cpu));
let matches = model.forward(&image0, &image1)?;

println!("match count = {}", matches.confidence.size()[0]);
# Ok::<(), loftr::LoftrError>(())
```

## Docs.rs

The published API docs are built with the `doc-only` feature so docs.rs can
render the crate without linking libtorch.