为汽车场景设计的深度估计算法评估新指标，更关注安全关键类别的表现。
Introducing a Class-Aware Metric for Monocular Depth Estimation: An Automotive Perspective
- 按物体类别加权评估，结合距离与安全重要性。
- 引入边缘角点特征和全局一致性保持机制提升评估精度。
- 适合自动驾驶领域开发者用于发现模型在危急场景中的缺陷。
度量单目深度估计模型的准确率不断提升，引发汽车领域的广泛关注。现有评估方法难以深入揭示模型在安全关键或未见类别上的表现。本文提出一种新型深度估计算法评估方法，包含三个核心组件：类别感知分量、边缘与角点图像特征分量、全局一致性保持分量。类别根据其在场景中的距离及对汽车应用的重要性进行加权。通过与传统指标对比、类别级分析及危险场景检索，实验表明该指标能提供更深层的模型性能洞察，同时满足安全关键需求。代码与权重已开源：https://github.com/leisemann/ca_mmde。
原文摘要 · Abstract (English)
The increasing accuracy reports of metric monocular depth estimation models lead to a growing interest from the automotive domain. Current model evaluations do not provide deeper insights into the models' performance, also in relation to safety-critical or unseen classes. Within this paper, we present a novel approach for the evaluation of depth estimation models. Our proposed metric leverages three components, a class-wise component, an edge and corner image feature component, and a global consistency retaining component. Classes are further weighted on their distance in the scene and on criticality for automotive applications. In the evaluation, we present the benefits of our metric through comparison to classical metrics, class-wise analytics, and the retrieval of critical situations. The results show that our metric provides deeper insights into model results while fulfilling safety-critical requirements. We release the code and weights on the following repository: https://github.com/leisemann/ca_mmde
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。