| | --- |
| | pretty_name: AstroVision |
| | viewer: false |
| | --- |
| | |
| | <a href="https://imgur.com/Vs3Rwin"><img src="https://i.imgur.com/Vs3Rwin.png" title="source: imgur.com" /></a> |
| |
|
| | --- |
| |
|
| | <a href="https://imgur.com/RjSwdG2"><img src="https://i.imgur.com/RjSwdG2.png" title="source: imgur.com" /></a> |
| |
|
| | # About |
| |
|
| | AstroVision is a first-of-a-kind, large-scale dataset of real small body images from both legacy and ongoing deep space missions, which currently features 115,970 densely annotated, real images of sixteen small bodies from eight missions. AstroVision was developed to facilitate the study of computer vision and deep learning for autonomous navigation in the vicinity of a small body, with speicial emphasis on training and evaluation of deep learning-based keypoint detection and feature description methods. |
| |
|
| | If you find our datasets useful for your research, please cite the [AstroVision paper](https://www.sciencedirect.com/science/article/pii/S0094576523000103): |
| |
|
| | ```bibtex |
| | @article{driver2023astrovision, |
| | title={{AstroVision}: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learning}, |
| | author={Driver, Travis and Skinner, Katherine and Dor, Mehregan and Tsiotras, Panagiotis}, |
| | journal={Acta Astronautica: Special Issue on AI for Space}, |
| | year={2023}, |
| | volume={210}, |
| | pages={393--410} |
| | } |
| | ``` |
| |
|
| | Please make sure to like the respository to show support! |
| |
|
| | # Data format |
| |
|
| | Following the popular [COLMAP data format](https://colmap.github.io/format.html), each data segment contains the files `images.bin`, `cameras.bin`, and `points3D.bin`, which contain the camera extrinsics and keypoints, camera intrinsics, and 3D point cloud data, respectively. |
| |
|
| | - `cameras.bin` encodes a dictionary of `camera_id` and [`Camera`](third_party/colmap/scripts/python/read_write_model.py) pairs. `Camera` objects are structured as follows: |
| | - `Camera.id`: defines the unique (and possibly noncontiguious) identifier for the `Camera`. |
| | - `Camera.model`: the camera model. We utilize the "PINHOLE" camera model, as AstroVision contains undistorted images. |
| | - `Camera.width` & `Camera.height`: the width and height of the sensor in pixels. |
| | - `Camera.params`: `List` of cameras parameters (intrinsics). For the "PINHOLE" camera model, `params = [fx, fy, cx, cy]`, where `fx` and `fy` are the focal lengths in $x$ and $y$, respectively, and (`cx`, `cy`) is the principal point of the camera. |
| |
|
| | - `images.bin` encodes a dictionary of `image_id` and [`Image`](third_party/colmap/scripts/python/read_write_model.py) pairs. `Image` objects are structured as follows: |
| | - `Image.id`: defines the unique (and possibly noncontiguious) identifier for the `Image`. |
| | - `Image.tvec`: $\mathbf{r}^\mathcal{C_ i}_ {\mathrm{BC}_ i}$, i.e., the relative position of the origin of the camera frame $\mathcal{C}_ i$ with respect to the origin of the body-fixed frame $\mathcal{B}$ expressed in the $\mathcal{C}_ i$ frame. |
| | - `Image.qvec`: $\mathbf{q}_ {\mathcal{C}_ i\mathcal{B}}$, i.e., the relative orientation of the camera frame $\mathcal{C}_ i$ with respect to the body-fixed frame $\mathcal{B}$. The user may call `Image.qvec2rotmat()` to get the corresponding rotation matrix $R_ {\mathcal{C}_ i\mathcal{B}}$. |
| | - `Image.camera_id`: the identifer for the camera that was used to capture the image. |
| | - `Image.name`: the name of the corresponding file, e.g., `00000000.png`. |
| | - `Image.xys`: contains all of the keypoints $\mathbf{p}^{(i)} _k$ in image $i$, stored as a ($N$, 2) array. In our case, the keypoints are the forward-projected model vertices. |
| | - `Image.point3D_ids`: stores the `point3D_id` for each keypoint in `Image.xys`, which can be used to fetch the corresponding `point3D` from the `points3D` dictionary. |
| | |
| | - `points3D.bin` enocdes a dictionary of `point3D_id` and [`Point3D`](third_party/colmap/scripts/python/read_write_model.py) pairs. `Point3D` objects are structured as follows: |
| | - `Point3D.id`: defines the unique (and possibly noncontiguious) identifier for the `Point3D`. |
| | - `Point3D.xyz`: the 3D-coordinates of the landmark in the body-fixed frame, i.e., $\mathbf{\ell} _{k}^\mathcal{B}$. |
| | - `Point3D.image_ids`: the ID of the images in which the landmark was observed. |
| | - `Point3D.point2D_idxs`: the index in `Image.xys` that corresponds to the landmark observation, i.e., `xy = images[Point3D.image_ids[k]].xys[Point3D.point2D_idxs[k]]` given some index `k`. |
| |
|
| | These three data containers, along with the ground truth shape model, completely describe the scene. |
| |
|
| | In addition to the scene geometry, each image is annotated with a landmark map, a depth map, and a visibility mask. |
| |
|
| | <a href="https://imgur.com/DGUC0ef"><img src="https://i.imgur.com/DGUC0ef.png" title="source: imgur.com" /></a> |
| |
|
| | - The _landmark map_ provides a consistent, discrete set of reference points for sparse correspondence computation and is derived by forward-projecting vertices from a medium-resolution (i.e., $\sim$ 800k facets) shape model onto the image plane. We classify visible landmarks by tracing rays (via the [Trimesh library](https://trimsh.org/)) from the landmarks toward the camera origin and recording landmarks whose line-of-sight ray does not intersect the 3D model. |
| | - The _depth map_ provides a dense representation of the imaged surface and is computed by backward-projecting rays at each pixel in the image and recording the depth of the intersection between the ray and a high-resolution (i.e., $\sim$ 3.2 million facets) shape model. |
| | - The _visbility mask_ provides an estimate of the non-occluded portions of the imaged surface. |
| |
|
| | **Note:** Instead of the traditional $z$-depth parametrization used for depth maps, we use the _absolute depth_, similar to the inverse depth parameterization. |
