ViBA: 基于几何和时间一致性的隐式捆绑调整用于鲁棒视觉匹配
ViBA: Implicit Bundle Adjustment with Geometric and Temporal Consistency for Robust Visual Matching
AI总结：
ViBA结合几何优化与特征学习，实现连续在线训练，通过隐式可微的几何残差框架提升视觉匹配的鲁棒性和精度，实测在EuRoC和UMA数据集上显著降低位移和旋转误差。
摘要：
现有图像关键点检测和描述方法依赖准确姿态和深度标注的数据集，限制了可扩展性和泛化能力，并常导致导航和定位性能下降。我们提出ViBA，一种可持续学习框架，整合几何优化与特征学习，用于在无约束视频流中进行连续在线训练。嵌入标准视觉里程计流程中，其包含一个隐式可微的几何残差框架：(i) 用于帧间对应关系的初始跟踪网络，(ii) 基于深度的异常值过滤，以及(iii) 可微的全局捆绑调整，通过最小化重投影误差联合优化相机姿态和特征位置。通过结合BA的几何一致性与跨帧的长期时间一致性，ViBA强制稳定的特征表示。我们在EuRoC和UMA数据集上评估ViBA。与SuperPoint+SuperGlue、ALIKED和LightGlue等最新方法相比，ViBA在序列中将平均绝对位移误差(ATE)减少12-18%，绝对旋转误差(ARE)减少5-10%，同时保持实时推理速度(36-91 FPS)。在未见过的序列上评估时，其保留超过90%的定位精度，展示了鲁棒泛化能力。这些结果表明，ViBA支持通过几何和时间一致性进行连续在线学习，持续改进现实场景中的导航和定位性能。
英文摘要：
Most existing image keypoint detection and description methods rely on datasets with accurate pose and depth annotations, limiting scalability and generalization, and often degrading navigation and localization performance. We propose ViBA, a sustainable learning framework that integrates geometric optimization with feature learning for continuous online training on unconstrained video streams. Embedded in a standard visual odometry pipeline, it consists of an implicitly differentiable geometric residual framework: (i) an initial tracking network for inter-frame correspondences, (ii) depth-based outlier filtering, and (iii) differentiable global bundle adjustment that jointly refines camera poses and feature positions by minimizing reprojection errors. By combining geometric consistency from BA with long-term temporal consistency across frames, ViBA enforces stable and accurate feature representations. We evaluate ViBA on EuRoC and UMA datasets. Compared with state-of-the-art methods such as SuperPoint+SuperGlue, ALIKED, and LightGlue, ViBA reduces mean absolute translation error (ATE) by 12-18% and absolute rotation error (ARE) by 5-10% across sequences, while maintaining real-time inference speeds (FPS 36-91). When evaluated on unseen sequences, it retains over 90% localization accuracy, demonstrating robust generalization. These results show that ViBA supports continuous online learning with geometric and temporal consistency, consistently improving navigation and localization in real-world scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
