ClaraVid

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Official repo for: ClaraVid: A Holistic Scene Reconstruction Benchmark From Aerial Perspective With Delentropy-Based Complexity Profiling - Accepted ICCV 2025

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 5 diverse environments: urban, urban high, rural, highway, and nature.

Delentropic Scene Profile (DSP) is a metric for estimating scene complexity from images, tailored for structured UAV mapping scenarios. DSP helps predict reconstruction difficulty.

Channel Log / TODOs

• All data uploaded
• Release dataset SDK
• Release pip package
• Release dataset splits
• Add Nerfstudio support
• Dataset download script
• Release DSP code (closer to conference)

Installation

Easiest way to install this package is to use pip:

pip install claravid 

Alternatively you can clone the repository and install it manually:

git clone https://github.com/rdbch/claravid_code.git
pip install -e . 

Examples

We provide 2 examples for this code base:

Dataset interface

In examples/demo.ipynb we provide an example for loading and exploring a scene and configuring the various flight parameters and modalities:

from claravid import ClaravidDataset

dataset = ClaravidDataset(
    root=Path('/path/to/claravid'),
    missions=['highway_1', ],     # see ClaravidMissions
    altitude=['low', ],           # see ClaravidAltitude
    direction=['v', 'h'],         # see ClaravidGridDirection
    fields=['rgb', 'pan_seg', 'depth', ...],
)
data = dataset[0]
{"rgb":..., "pan_seg":..., "depth":..., ...}

3D Manipulation

In examples/pcl_project.py we provide an example for loading the scene PCL and projecting it to back to a certain frame. This serves as an example on how to handle extrinsics, 3D un/projection and manipulating scene pointclouds.

Bibtex

If you found this work useful, please cite us as:

@misc{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}
}