Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on
  Aerial Images
Understanding the geometric and semantic properties of the scene is crucial
in autonomous navigation and particularly challenging in the case of Unmanned
Aerial Vehicle (UAV) navigation. Such information may be by obtained by
estimating depth and semantic segmentation maps of the surrounding environment
and for their practical use in autonomous navigation, the procedure must be
performed as close to real-time as possible. In this paper, we leverage
monocular cameras on aerial robots to predict depth and semantic maps in
low-altitude unstructured environments. We propose a joint deep-learning
architecture that can perform the two tasks accurately and rapidly, and
validate its effectiveness on MidAir and Aeroscapes benchmark datasets. Our
joint-architecture proves to be competitive or superior to the other single and
joint architecture methods while performing its task fast predicting 20.2 FPS
on a single NVIDIA quadro p5000 GPU and it has a low memory footprint. All
codes for training and prediction can be found on this link:
https://github.com/Malga-Vision/Co-SemDepth
67e4276e59b2d29cb2b20276
Yara AlaaEldin
extracted_confirmed
2025-03-26T16:41:18.789Z
6780384de11224514e5f1059
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Rxkp1GnjCf-u5AlE1-4gi.png
Yara AlaaEldin
user
yaraalaa0
67e4276e59b2d29cb2b20277
Francesca Odone
Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on
  Aerial Images
Understanding the geometric and semantic properties of the scene is crucial
in autonomous navigation and particularly challenging in the case of Unmanned
Aerial Vehicle (UAV) navigation. Such information may be by obtained by
estimating depth and semantic segmentation maps of the surrounding environment
and for their practical use in autonomous navigation, the procedure must be
performed as close to real-time as possible. In this paper, we leverage
monocular cameras on aerial robots to predict depth and semantic maps in
low-altitude unstructured environments. We propose a joint deep-learning
architecture that can perform the two tasks accurately and rapidly, and
validate its effectiveness on MidAir and Aeroscapes benchmark datasets. Our
joint-architecture proves to be competitive or superior to the other single and
joint architecture methods while performing its task fast predicting 20.2 FPS
on a single NVIDIA quadro p5000 GPU and it has a low memory footprint. All
codes for training and prediction can be found on this link:
https://github.com/Malga-Vision/Co-SemDepth
A joint deep-learning architecture predicts depth and semantic maps using monocular cameras for real-time autonomous UAV navigation, outperforming other methods with high speed and low memory usage.
monocular cameras joint deep-learning architecture depth prediction semantic segmentation MidAir dataset Aeroscapes dataset NVIDIA quadro p5000 GPU real-time navigation low memory footprint