3 citations · 3 across the 3 of their papers we have counts for
9 papers
TT-NF: Tensor Train Neural Fields
Anton Obukhov, Mikhail Usvyatsov, Christos Sakaridis +2
Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations. In this paper, we…
P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior
Vaishakh Patil, Christos Sakaridis, Alexander Liniger +1
Monocular depth estimation is vital for scene understanding and downstream tasks. We focus on the supervised setup, in which ground-truth depth is available only at training time.…
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather
Martin Hahner, Christos Sakaridis, Dengxin Dai +1
This work addresses the challenging task of LiDAR-based 3D object detection in foggy weather. Collecting and annotating data in such a scenario is very time, labor and cost intensi…
Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
Christos Sakaridis, Dengxin Dai, Luc Van Gool
We address the problem of semantic nighttime image segmentation and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreo…
Semantic Understanding of Foggy Scenes with Purely Synthetic Data
Martin Hahner, Dengxin Dai, Christos Sakaridis +2
This work addresses the problem of semantic scene understanding under foggy road conditions. Although marked progress has been made in semantic scene understanding over the recent…
Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
Christos Sakaridis, Dengxin Dai, Luc Van Gool
Most progress in semantic segmentation reports on daytime images taken under favorable illumination conditions. We instead address the problem of semantic segmentation of nighttime…