Dataset Generation Process for Enhancing Depth Estimation Network in Autonomous Driving
To ensure the safety of autonomous vehicles, accurately perceiving the spatial information of the surrounding environment is crucial. Supervised learning-based camera depth estimation networks can be used for this purpose. However, training these networks requires high-quality depth datasets, but ex...
Saved in:
Main Authors: | Jinsu Ha, Kichun Jo |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10654251/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The EDI Multi-Modal Simultaneous Localization and Mapping Dataset (EDI-SLAM)
by: Peteris Racinskis, et al.
Published: (2025-01-01) -
High-Fidelity Depth Map Reconstruction System With RGB-Guided Super Resolution CNN and Cross-Calibrated Chaos LiDAR
by: Yu-Chun Ding, et al.
Published: (2025-01-01) -
Methods for Improving Point Cloud Authenticity in LiDAR Simulation for Autonomous Driving: A Review
by: Yanzhao Yang, et al.
Published: (2025-01-01) -
Initial Pose Estimation Method for Robust LiDAR-Inertial Calibration and Mapping
by: Eun-Seok Park , et al.
Published: (2024-12-01) -
Enhancing LiDAR Mapping with YOLO-Based Potential Dynamic Object Removal in Autonomous Driving
by: Seonghark Jeong, et al.
Published: (2024-11-01)