通过关键点检测与序列信息提升场景坐标回归精度与速度
Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential Information
- 统一架构同时处理场景编码与显著关键点检测，聚焦有效区域
- 单帧模式下召回率提升6.4%，推理速度从56Hz增至90Hz
- 序列模式下召回率再增11%，适合高精度实时定位场景
场景坐标回归（SCR）是一种利用深度神经网络直接回归2D-3D对应关系以实现相机位姿估计的视觉定位技术。然而，现有SCR方法依赖隐式三角化，在重复纹理和无意义区域面临挑战。本文提出一种高效准确的SCR系统：设计统一架构联合进行场景编码与显著关键点检测，优先编码信息丰富区域，显著提升计算效率；引入序列信息利用机制，贯穿映射与重定位阶段，增强隐式三角化能力，尤其在重复纹理环境中表现更优。在室内外数据集上的全面实验表明，该方法优于当前最先进（SOTA）的SCR方法。单帧重定位模式下，召回率相比基线提升6.4%，运行速度由56Hz提升至90Hz；序列模式下召回率进一步提升11%，同时保持原有效率。
原文摘要 · Abstract (English)
Scene Coordinate Regression (SCR) is a visual localization technique that utilizes deep neural networks (DNN) to directly regress 2D-3D correspondences for camera pose estimation. However, current SCR methods often face challenges in handling repetitive textures and meaningless areas due to their reliance on implicit triangulation. In this paper, we propose an efficient and accurate SCR system. Compared to existing SCR methods, we propose a unified architecture for both scene encoding and salient keypoint detection, allowing our system to prioritize the encoding of informative regions. This design significantly improves computational efficiency. Additionally, we introduce a mechanism that utilizes sequential information during both mapping and relocalization. The proposed method enhances the implicit triangulation, especially in environments with repetitive textures. Comprehensive experiments conducted across indoor and outdoor datasets demonstrate that the proposed system outperforms state-of-the-art (SOTA) SCR methods. Our single-frame relocalization mode improves the recall rate of our baseline by 6.4% and increases the running speed from 56Hz to 90Hz. Furthermore, our sequence-based mode increases the recall rate by 11% while maintaining the original efficiency.
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