効率的なキーポイント検出と系列情報によるシーン座標回帰の強化
Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential Information
シーン座標回帰において、キーポイント検出とシーンエンコーディングを統合し、系列情報を活用することで、繰り返しテクスチャ環境での精度と効率を向上させた。
著者: Kuan Xu, Zeyu Jiang, Haozhi Cao, Shenghai Yuan, Chen Wang, Lihua Xie
分類: cs.RO, cs.CV
原文アブストラクト
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.
