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Jul, 2023
RoboDepth挑战:稳健深度估计方法和进展
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
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Lingdong Kong, Yaru Niu, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng...
TL;DR
这篇论文总结了RoboDepth挑战中的获胜解决方案,重点关注鲁棒的自监督和全监督深度估计,涉及了深度估计、挑战赛、深度预测、超分辨率和降噪等方面的创新设计。
Abstract
Accurate
depth estimation
under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical applications. Existing
depth estimation<
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