align3d

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

★ 13 Open GitHub ↗

Rust-based 3D point cloud alignment

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Align3D - Iterative Closest Point in Rust

Align3D provides alignment of range images and point clouds using the Iterative Closest Point (ICP) algorithm.

It provides functionalities:

* Alignment of range images and point clouds using the Iterative Closest Point (ICP) algorithm.
* Reading and writing of .ply and .off files for easy data exchange.
* Support for the TUM and IL-RGBD datasets for input.
* Visualization of point clouds, surfels, and other geometries to inspect the results.
* Processing utilities like normal vector computation for range images and bilateral filtering for depth images.
* Computation of odometry metrics

Align3D leverages several Rust libraries to provide its functionality:

* [ndarray](https://github.com/rust-ndarray/ndarray) for efficient multi-dimensional array processing;
* [nalgebra](https://nalgebra.org/) for linear algebra operations;
* [vulkano](https://vulkano.rs/) with Vulkan for high-performance computing and rendering capabilities.
* [image](https://github.com/image-rs/image) is used for image processing tasks. By harnessing the capabilities of these libraries

## Getting it

Use Cargo:

```shell
$ cargo add align3d
```

OR, to install with visualization features, use:

```shell
$ cargo add align3d --features viz
```

## Sample use

The following code does the following:

* loads the IndoorLidarDataset;
* computes the odometry for 20 frames;
* display the metrics comparing with the ground truth;
* and shows the alignment results.

```rust
use align3d::{
    bilateral::BilateralFilter,
    icp::{multiscale::MultiscaleAlign, MsIcpParams},
    io::dataset::{IndoorLidarDataset, RgbdDataset, SubsetDataset},
    metrics::TransformMetrics,
    range_image::RangeImageBuilder,
    trajectory_builder::TrajectoryBuilder,
    viz::rgbd_dataset_viewer::RgbdDatasetViewer,
};

fn main() -> Result<(), Box<dyn std::error::Error + 'static>> {
    // Load the dataset
    let dataset = Box::new(SubsetDataset::new(
        Box::new(IndoorLidarDataset::load("tests/data/indoor_lidar/bedroom")?),
        (0..20).collect(),
    ));

// RangeImageBuilder composes the processing steps when loading RGB-D frames (or `RangeImage`).
    let range_image_transform = RangeImageBuilder::default()
        .with_intensity(true) // Use intensity besides RGB
        .with_normals(true) // Compute the normals
        .with_bilateral_filter(Some(BilateralFilter::default())) // Apply bilateral filter
        .pyramid_levels(3); // Compute 3-level Gaussian pyramid.

// Default ICP parameters
    let icp_params = MsIcpParams::default();

// TrajectoryBuilder accumulates the per-frame alignment to form the odometry of the camera poses.
    let mut traj_builder = TrajectoryBuilder::default();

// Use the `.build()` method to create a RangeImage pyramid.
    let mut prev_frame = range_image_transform.build(dataset.get(0).unwrap());

// Iterate over the dataset
    for i in 1..dataset.len() {
        let current_frame = range_image_transform.build(dataset.get(i).unwrap());
        
        // Perform ICP alignment
        let icp = MultiscaleAlign::new(icp_params.clone(), &prev_frame).unwrap();
        let transform = icp.align(&current_frame);

// Accumulate transformations for obtaining odometry
        traj_builder.accumulate(&transform, Some(i as f32));
        prev_frame = current_frame;
    }

// Compute metrics in relation to the ground truth
    // Get the predicted trajectory
    let pred_trajectory = traj_builder.build();
    
    // Get the ground truth trajectory
    let gt_trajectory = &dataset
        .trajectory()
        .expect("Dataset has no trajectory")
        .first_frame_at_origin();

// Compute the metrics
    let metrics = TransformMetrics::mean_trajectory_error(&pred_trajectory, &gt_trajectory)?;
    println!("Mean trajectory error: {}", metrics);

// Visualization part
    RgbdDatasetViewer::new(dataset)
        .with_trajectory(pred_trajectory.clone())
        .run();

Ok(())
}

```

```txt
Mean trajectory error: angle: 1.91°, translation: 0.03885
```

and show a window like this:

![](https://raw.githubusercontent.com/otaviog/align3d/main/resources/imgs/2023-04-07-16-26-03.png)

(move the camera using WASD controls)

# Benchmarking

| Functionality   | Input desc.                                    |[min, mean, max]                 |
|-----------------|------------------------------------------------|---------------------------------|
| ImageIcp        | 1 640x480 input                                | [38.423 ms 38.576 ms 38.732 ms] |
| kdtree          | 500000 database vs 500000 queries of 3D points | [101.48 ms 101.75 ms 102.04 ms] |
| compute_normals | 640x480 RGB-D frame                            | [1.1587 ms 1.1778 ms 1.2005 ms] |

* Hardware: 11th Gen Intel® Core™ i7-11800H @ 2.30GHz × 16

# Contributing

Contributions to Align3D are welcome! If you find any issues or have suggestions for improvements, please create a new issue or submit a pull request.

# License

Align3D is licensed under the MIT License.

# Release Plan

Align3D is an experimental project that showcases the potential of using Rust for writing computer vision applications.
While still being a experimental project, it shows the versatility and performance benefits that Rust offers compared to the traditional combination of C++ and Python commonly used in computer vision and machine learning.

The project has the following Road map:

* [x] Bug fixes in PCL ICP.
* [x] Optimize Image Icp performance
* [ ] Python bindings

align3d

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README