14 citations · 34 across the 8 of their papers we have counts for
8 papers
Future Dynamic 3D Reconstruction: Toward 3D World Modeling with Disentangled Ego-Motion
Nils Morbitzer, Jonathan Evers, Artem Savkin +4
Forecasting the evolution of dynamic environments is crucial for autonomous agents. While generative world models have achieved high photorealism in 2D video synthesis by mixing eg…
Adversarial Appearance Learning in Augmented Cityscapes for Pedestrian Recognition in Autonomous Driving
Artem Savkin, Thomas Lapotre, Kevin Strauss +2
In the autonomous driving area synthetic data is crucial for cover specific traffic scenarios which autonomous vehicle must handle. This data commonly introduces domain gap between…
Unsupervised Traffic Scene Generation with Synthetic 3D Scene Graphs
Artem Savkin, Rachid Ellouze, Nassir Navab +1
Image synthesis driven by computer graphics achieved recently a remarkable realism, yet synthetic image data generated this way reveals a significant domain gap with respect to rea…
Lidar Upsampling with Sliced Wasserstein Distance
Artem Savkin, Yida Wang, Sebastian Wirkert +2
Lidar became an important component of the perception systems in autonomous driving. But challenges of training data acquisition and annotation made emphasized the role of the sens…
Attention-based Adversarial Appearance Learning of Augmented Pedestrians
Kevin Strauss, Artem Savkin, Federico Tombari
Synthetic data became already an essential component of machine learning-based perception in the field of autonomous driving. Yet it still cannot replace real data completely due t…
KLIEP-based Density Ratio Estimation for Semantically Consistent Synthetic to Real Images Adaptation in Urban Traffic Scenes
Artem Savkin, Federico Tombari
Synthetic data has been applied in many deep learning based computer vision tasks. Limited performance of algorithms trained solely on synthetic data has been approached with domai…
Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation
Mert Keser, Artem Savkin, Federico Tombari
Synthetic data generation is an appealing approach to generate novel traffic scenarios in autonomous driving. However, deep learning perception algorithms trained solely on synthet…
Automated Scene Flow Data Generation for Training and Verification
Oliver Wasenmüller, René Schuster, Didier Stricker +6
Scene flow describes the 3D position as well as the 3D motion of each pixel in an image. Such algorithms are the basis for many state-of-the-art autonomous or automated driving fun…