EAR-Net: Pursuing End-to-End Absolute Rotations from Multi-View Images

📄 arXiv: 2310.10051v2 📥 PDF

作者: Yuzhen Liu, Qiulei Dong

分类: cs.CV

发布日期: 2023-10-16 (更新: 2024-03-08)

💡 一句话要点

提出EAR-Net以解决多视角图像的绝对旋转估计问题

🎯 匹配领域: 支柱六：视频提取与匹配 (Video Extraction)

关键词: 绝对旋转估计 多视角图像 深度学习 计算机视觉 端到端方法 置信度机制 机器人导航 增强现实

📋 核心要点

  1. 现有的多阶段绝对旋转估计方法容易导致误差累积，影响最终结果的准确性。
  2. 本文提出的EAR-Net通过端到端的方式，利用深度学习同时预测相对旋转及其置信度，提升了估计的准确性。
  3. 在三个公开数据集上的实验结果显示，EAR-Net在准确性和速度上均大幅超越了当前的最先进方法。

📝 摘要（中文）

绝对旋转估计是3D计算机视觉中的一个重要课题。现有文献通常采用多阶段（至少两阶段）估计策略，导致各个操作引入的误差累积，从而降低最终的全局旋转估计精度。为了解决这一问题，本文提出了一种基于深度神经网络的端到端绝对旋转估计方法，称为EAR-Net。该方法包括一个极线置信图构建模块和一个置信感知旋转平均模块，能够有效处理异常值情况。实验结果表明，EAR-Net在准确性和速度上均显著优于现有最先进的方法。

🔬 方法详解

问题定义：本文旨在解决多视角图像中的绝对旋转估计问题。现有方法采用多阶段策略，导致误差累积，影响最终的全局旋转估计精度。

核心思路：EAR-Net通过端到端的深度学习框架，设计了极线置信图构建模块和置信感知旋转平均模块，能够同时预测相对旋转及其置信度，从而提高估计的鲁棒性。

技术框架：EAR-Net的整体架构包括两个主要模块：极线置信图构建模块负责生成加权图，置信感知旋转平均模块则基于该图进行绝对旋转的预测。

关键创新：EAR-Net的核心创新在于引入了置信度机制，使得相对旋转的估计能够有效处理异常值，显著提升了旋转估计的准确性和稳定性。

关键设计：在网络设计上，EAR-Net采用了可微分的置信感知旋转平均模块，确保了整个网络的端到端训练。此外，损失函数的设计考虑了置信度的影响，使得训练过程更加高效。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，EAR-Net在三个公开数据集上均显著优于现有最先进的方法，准确性提升幅度达到XX%，速度提升幅度达到YY%。这些结果验证了EAR-Net在处理复杂场景下的有效性和高效性。

🎯 应用场景

该研究在3D计算机视觉领域具有广泛的应用潜力，尤其是在机器人导航、增强现实和虚拟现实等场景中。通过提高绝对旋转估计的准确性，EAR-Net能够为多视角图像处理提供更为可靠的基础，推动相关技术的发展与应用。

📄 摘要（原文）

Absolute rotation estimation is an important topic in 3D computer vision. Existing works in literature generally employ a multi-stage (at least two-stage) estimation strategy where multiple independent operations (feature matching, two-view rotation estimation, and rotation averaging) are implemented sequentially. However, such a multi-stage strategy inevitably leads to the accumulation of the errors caused by each involved operation, and degrades its final estimation on global rotations accordingly. To address this problem, we propose an End-to-end method for estimating Absolution Rotations from multi-view images based on deep neural Networks, called EAR-Net. The proposed EAR-Net consists of an epipolar confidence graph construction module and a confidence-aware rotation averaging module. The epipolar confidence graph construction module is explored to simultaneously predict pairwise relative rotations among the input images and their corresponding confidences, resulting in a weighted graph (called epipolar confidence graph). Based on this graph, the confidence-aware rotation averaging module, which is differentiable, is explored to predict the absolute rotations. Thanks to the introduced confidences of the relative rotations, the proposed EAR-Net could effectively handle outlier cases. Experimental results on three public datasets demonstrate that EAR-Net outperforms the state-of-the-art methods by a large margin in terms of accuracy and speed.