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Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
(2018)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2018 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIIIThis work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation ...Conference Paper -
Real-time 3D Traffic Cone Detection for Autonomous Driving
(2019)2019 IEEE Intelligent Vehicles Symposium (IV)Conference Paper -
Is Image Super-resolution Helpful for Other Vision Tasks?
(2016)2016 IEEE Winter Conference on Applications of Computer Vision (WACV)Despite the great advances made in the field of image super-resolution (ISR) during the last years, the performance has merely been evaluated perceptually. Thus, it is still unclear whether ISR is helpful for other vision tasks. In this paper, we present the first comprehensive study and analysis of the usefulness of ISR for other vision applications. In particular, six ISR methods are evaluated on four popular vision tasks, namely edge ...Conference Paper -
Depth Estimation from Monocular Images and Sparse Radar Data
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)In this paper, we explore the possibility of achieving a more accurate depth estimation by fusing monocular images and Radar points using a deep neural network. We give a comprehensive study of the fusion between RGB images and Radar measurements from different aspects and proposed a working solution based on the observations. We find that the noise existing in Radar measurements is one of the main key reasons that prevents one from ...Conference Paper -
Learning Accurate and Human-Like Driving using Semantic Maps and Attention
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. To tackle the first issue we exploit semantic and visual maps from HERE Technologies and augment the existing Drive360 dataset with such. The maps are used in an attention mechanism that promotes segmentation confidence masks, thus focusing the network on semantic classes in the image that are important for the current driving ...Conference Paper -
Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection
(2021)2021 IEEE Winter Conference on Applications of Computer Vision (WACV)Point cloud semantic segmentation plays an essential role in autonomous driving, providing vital information about drivable surfaces and nearby objects that can aid higher level tasks such as path planning and collision avoidance. While current 3D semantic segmentation networks focus on convolutional architectures that perform great for well represented classes, they show a significant drop in performance for underrepresented classes that ...Conference Paper -
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process. To address this issue, we present a framework for semi-supervised semantic segmentation, which is enhanced by self-supervised monocular depth estimation from unlabeled image sequences. In particular, we propose three key contributions: ...Conference Paper -
ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Level 5 autonomy for self-driving cars requires a robust visual perception system that can parse input images under any visual condition. However, existing semantic segmentation datasets are either dominated by images captured under normal conditions or are small in scale. To address this, we introduce ACDC, the Adverse Conditions Dataset with Correspondences for training and testing semantic segmentation methods on adverse visual conditions. ...Conference Paper -
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)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 intensive. In this paper, we tackle this problem by simulating physically accurate fog into clear-weather scenes, so that the abundant existing real datasets captured in clear weather can be repurposed for our task. Our contributions are twofold: 1) We develop a ...Conference Paper -
Hyperspectral Image Super-Resolution with RGB Image Super-Resolution as an Auxiliary Task
(2022)2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)This work studies Hyperspectral image (HSI) super-resolution (SR). HSI SR is characterized by high-dimensional data and a limited amount of training examples. This raises challenges for training deep neural networks that are known to be data hungry. This work addresses this issue with two contributions. First, we observe that HSI SR and RGB image SR are correlated and develop a novel multi-tasking network to train them jointly so that the ...Conference Paper