--- license: cc-by-nc-sa-4.0 pretty_name: ClaraVid size_categories: - 10K By submitting this request, you confirm that you have read and agree to the following terms: • You have reviewed and accept the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/) • You will not employ the dataset in any context that could directly or indirectly cause harm to individuals or communities • You will properly acknowledge and cite the dataset in any derivative works or publications extra_gated_fields: First Name: text Surname: text Affiliation: text Country: country I confirm that I have read and understood the presented terms and license: checkbox extra_gated_button_content: Submit Access Request task_categories: - image-to-3d - depth-estimation - image-segmentation tags: - UAV - Aerial - Semantic Mapping - 3D Reconstruction - Neural Recontruction - NERF - Gaussian Splatting - Urban - Highway - Nature - Rural --- # ClaraVid Dataset [![Project Page](https://img.shields.io/badge/Project%20Page-ClaraVid-blue?style=flat&logo=github)](https://rdbch.github.io/claravid/) [![Dataset SDK](https://img.shields.io/badge/Dataset%20SDK-ClaraVid-green?style=flat&logo=github)](https://github.com/rdbch/claravid_code) [![arXiv Preprint](https://img.shields.io/badge/arXiv-2503.17856-b31b1b?style=flat&logo=arXiv&logoColor=white)](https://arxiv.org/abs/2503.17856) Official repo for: *ClaraVid: A Holistic Scene Reconstruction Benchmark From Aerial Perspective With Delentropy-Based Complexity Profiling* - Accepted ICCV 2025 > If you find this useful, please consider giving us a like ❤ ![ClaraVid Overview](https://rdbch.github.io/claravid/images/overview.jpg) **ClaraVid** is a synthetic dataset built for semantic and geometric neural reconstruction from low altitude UAV/aerial imagery. It contains **16,917 multimodal frames** collected across **8 UAV missions** over diverse environments: urban, urban high, rural, highway, and nature. Each mission features **3 viewpoints** and altitude levels, simulating multi-UAV operations. The dataset spans *1.8km^2*, with an average mission coverage of *0.22km^2*. It includes visual measurements at **4032x3024** resolution for *RGB images, metric depth maps, panoptic(semantic and instance) segmentation and dynamic object masks*. Additionally in contains *scene level pointcloud* and *camera calibration(intrinsic and extrinsic)*. ## Channel Log / TODOs - [x] All data uploaded - [x] Release dataset SDK - [x] Release pip package - [ ] Release dataset splits - [ ] Add Nerfstudio support - [ ] Dataset download script - [ ] Release DSP code (closer to conference) ## 3. Download Clone this repository and extract the data archive in the same folders. The archives were compressed using 7-Zip. We will provide a download script in the near future. ## 4. Usage We provide a dataset SDK on [GitHub](https://github.com/rdbch/claravid_code). You can simply install it using: ``` pip install claravid ``` ## 5. Dataset structure All collection missions follow a grid pattern with both vertical and horizontal passes at a constant altitude, with a few seconds between consecutive frames. ``` claravid/ ├── 001_rural_1/ # mission 1 │ ├── left_rgb/ │ │ ├── 45deg_low_h/ # 3 different viewpoints (pitch&altitude) & flying orientation in grid (horizontal or vertical passes) │ │ │ ├── 000000.jpg │ │ │ └── ... │ │ ├── 45deg_low_v/ │ │ │ └── ... │ │ ├── 55deg_mid_h/ │ │ │ └── ... │ │ ├── 55deg_mid_v/ │ │ │ └── ... │ │ ├── 90deg_high_h/ │ │ │ └── ... │ │ └── 90deg_high_v/ │ │ └── ... │ ├── depth/ # metric depth │ │ └── ... │ ├── panoptic_seg/ # instance (buildings, humans and vehicles) & semantic segmentation │ │ └── ... │ ├── semantics_colormap/ # semantic segmentation - RGB color version │ │ └── ... │ ├── dynamic_mask/ │ │ └── ... │ ├── extrinsics/ │ │ └── ... │ └── scene_pcl/ # scene level PCL (color, semantic, instance) @ various resolutions (30cm, 50cm, ...) │ ├── panoptic_seg/ │ │ ├── global_fused_30cm.ply │ └── ... ├── 002_rural_2/ # mission 2... │ └── ... └── ... ``` ## 6. Mission Overview ![ClaraVid Mission Overview](https://rdbch.github.io/claravid/images/more_claravid_overview.jpg) ## 6. Data format | **Modality** | **Directory** | **Extension** | **Description** | |-----------------------|---------------|---------------|-----------------| | RGB | left_rgb | .jpg | 4032 x 3024 | | Depth | depth | .png | metric depth - \[0-1000\]m | | Panoptic Segmentation | panoptic_seg | .png | instance (buildings, humans and vehicles) + semantic mask | | Dynamic mask | dynamic_mask | .png | binary mask for objects that move (dynamic_elements == 0) | | Camera Extrinsics | extrinsics | .json | in scene space (metric) | | ScenePointcloud | scene_pcl | .ply | scene pointclouds in scene space | Please refer to our [SDK](https://github.com/rdbch/claravid_code/blob/main/claravid/dataset.py) for more details regarding the data format. ## 7. BibTex If you find our work useful, please consider citing: ``` @article{beche2025claravid, title={ClaraVid: A Holistic Scene Reconstruction Benchmark From Aerial Perspective With Delentropy-Based Complexity Profiling}, author={Beche, Radu and Nedevschi, Sergiu}, journal={arXiv preprint arXiv:2503.17856}, year={2025} } ```