构建新数据集，评估视角变化对单目深度估计的影响
ViewpointDepth: A New Dataset for Monocular Depth Estimation Under Viewpoint Shifts
- 基于同形变换与目标检测构建真实深度标签
- 在多视角道路场景中发现现有模型性能显著下降
- 适合关注自动驾驶视觉鲁棒性的研究者
单目深度估计是自动驾驶等计算机视觉应用中的关键任务。尽管该领域进展显著，但视角变化对模型性能的影响仍缺乏深入研究。本文提出一种新型数据集与评估方法，量化不同相机位置和姿态对单目深度估计的影响。通过基于同形变换与目标检测的真值构建策略，避免了昂贵的激光雷达传感器依赖。我们采集了来自多个视角的多样化道路场景数据，并用于评估现代深度估计模型在几何变换下的鲁棒性。在公开数据集上验证策略有效性后，揭示了当前模型的局限性，强调了在真实应用中考虑视角变化的重要性。
原文摘要 · Abstract (English)
Monocular depth estimation is a critical task for autonomous driving and many other computer vision applications. While significant progress has been made in this field, the effects of viewpoint shifts on depth estimation models remain largely underexplored. This paper introduces a novel dataset and evaluation methodology to quantify the impact of different camera positions and orientations on monocular depth estimation performance. We propose a ground truth strategy based on homography estimation and object detection, eliminating the need for expensive LIDAR sensors. We collect a diverse dataset of road scenes from multiple viewpoints and use it to assess the robustness of a modern depth estimation model to geometric shifts. After assessing the validity of our strategy on a public dataset, we provide valuable insights into the limitations of current models and highlight the importance of considering viewpoint variations in real-world applications.
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