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Found 9 papers, 4 papers with code
Leveraging a realistic synthetic database to learn Shape-from-Shading for estimating the colon depth in colonoscopy images
The network was trained by a custom synthetic colonoscopy database herein constructed and released, composed of 248, 400 frames (47 videos), with depth annotations at the level of pixels.
NECO: NEural Collapse Based Out-of-distribution detection
Detecting out-of-distribution (OOD) data is a critical challenge in machine learning due to model overconfidence, often without awareness of their epistemological limits.
InfraParis: A multi-modal and multi-task autonomous driving dataset
Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes.
A study of deep perceptual metrics for image quality assessment
Several metrics exist to quantify the similarity between images, but they are inefficient when it comes to measure the similarity of highly distorted images.
Does it work outside this benchmark? Introducing the Rigid Depth Constructor tool, depth validation dataset construction in rigid scenes for the masses
Finally, we take the example of UAV videos, on which we test two depth algorithms that were initially tested on KITTI and show that the drone context is dramatically different from in-car videos.
End-to-end depth from motion with stabilized monocular videos
We propose a depth map inference system from monocular videos based on a novel dataset for navigation that mimics aerial footage from gimbal stabilized monocular camera in rigid scenes.
Learning structure-from-motion from motion
We then present results on a synthetic dataset that we believe to be more representative of typical UAV scenes.
Multi range Real-time depth inference from a monocular stabilized footage using a Fully Convolutional Neural Network
Using a neural network architecture for depth map inference from monocular stabilized videos with application to UAV videos in rigid scenes, we propose a multi-range architecture for unconstrained UAV flight, leveraging flight data from sensors to make accurate depth maps for uncluttered outdoor environment.
Binary Distance Transform to Improve Feature Extraction
This paper proposes the application of binary distance transform on the original dataset to add information to texture representation and consequently improve recognition.
