A Novel Panorama Depth Estimation Framework for Autonomous Driving Scenarios Based on a Vision Transformer
An accurate panorama depth estimation result is crucial to risk perception in autonomous driving practice. In this paper, an innovative framework is presented to address the challenges of imperfect observation and projection fusion in panorama depth estimation, enabling the accurate capture of dista...
Saved in:
Main Authors: | Yuqi Zhang, Liang Chu, Zixu Wang, He Tong, Jincheng Hu, Jihao Li |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-10-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/24/21/7013 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
CAPDepth: 360 Monocular Depth Estimation by Content-Aware Projection
by: Xu Gao, et al.
Published: (2025-01-01) -
Dataset Generation Process for Enhancing Depth Estimation Network in Autonomous Driving
by: Jinsu Ha, et al.
Published: (2024-01-01) -
Depth control of autonomous underwater vehicle using deep reinforcement learning
by: Rizhong WANG, et al.
Published: (2020-12-01) -
Esportes de combate: um panorama sociocultural
by: Flávio Py Mariante Neto
Published: (2009-03-01) -
Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control
by: Rodrigo Gutiérrez-Moreno, et al.
Published: (2024-12-01)