厳しい条件下での堅牢な単眼深度推定
Robust Monocular Depth Estimation under Challenging Conditions
夜間や雨天など厳しい照明・天候条件下でも信頼できる単眼深度推定手法md4allを提案。複雑なサンプルを生成し、元画像で損失を計算することで、単一モデルが多様な条件で高精度を実現。
著者: Stefano Gasperini, Nils Morbitzer, HyunJun Jung, Nassir Navab, Federico Tombari
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
While state-of-the-art monocular depth estimation approaches achieve impressive results in ideal settings, they are highly unreliable under challenging illumination and weather conditions, such as at nighttime or in the presence of rain. In this paper, we uncover these safety-critical issues and tackle them with md4all: a simple and effective solution that works reliably under both adverse and ideal conditions, as well as for different types of learning supervision. We achieve this by exploiting the efficacy of existing methods under perfect settings. Therefore, we provide valid training signals independently of what is in the input. First, we generate a set of complex samples corresponding to the normal training ones. Then, we train the model by guiding its self- or full-supervision by feeding the generated samples and computing the standard losses on the corresponding original images. Doing so enables a single model to recover information across diverse conditions without modifications at inference time. Extensive experiments on two challenging public datasets, namely nuScenes and Oxford RobotCar, demonstrate the effectiveness of our techniques, outperforming prior works by a large margin in both standard and challenging conditions. Source code and data are available at: https://md4all.github.io.
