Robust Depth Estimation in Foggy Environments Combining RGB Images and mmWave Radar-Reference-Cited by-同舟云学术

Robust Depth Estimation in Foggy Environments Combining RGB Images and mmWave Radar

Published:2022-12 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Symposium on Multimedia (ISM)
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Author:

Xiong Mengchen¹,Xu Xiao¹,Yang Dong¹,Steinbach Eckehard¹

Affiliation:

1. Technical University of Munich,School of Computation, Information and Technology,Department of Computer Engineering Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI),Munich,Germany

Funder

Deutsche Forschungsgemeinschaft

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10019600/10019610/10019611.pdf?arnumber=10019611

Reference36 articles.

1. Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios

2. Deeper Depth Prediction with Fully Convolutional Residual Networks

3. Influences of weather phenomena on automotive laser radar systems

4. Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

5. R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes

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2. Robust Monocular Depth Estimation under Challenging Conditions;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

3. Hierarchical Attention-Based Sensor Fusion Strategy for Depth Estimation in Diverse Weather;International Journal of Semantic Computing;2023-07-25

4. SRI-Graph: A Novel Scene-Robot Interaction Graph for Robust Scene Understanding;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29