论文标题
相机重新定位的快速轻巧的场景回归器
Fast and Lightweight Scene Regressor for Camera Relocalization
论文作者
论文摘要
涉及先前3D重建的摄像机重新定位在许多混合现实和机器人应用中起着至关重要的作用。直接估算相对于预先构建的3D模型的相机姿势对于多种存储和/或通信带宽的应用程序可能会非常昂贵。尽管最近的场景和绝对姿势回归方法在有效的相机定位方面已经流行，但其中大多数是计算资源密集型，并且难以获得具有高精度约束的实时推断。这项研究提出了一种简单的场景回归方法，该方法仅需要一个多层感知器网络来映射场景坐标，以实现准确的相机姿势估计。提出的方法使用稀疏的描述符来回归场景坐标，而不是密集的RGB图像。稀疏功能的使用提供了几个优势。首先，所提出的回归网络大大比以前的研究中报道的网络小得多。这使我们的系统高效且可扩展。其次，预构建的3D型号提供了最可靠，最强大的2D-3D匹配项。因此，向他们学习可以提高对等效特征的认识，并大大提高概括性能。提供了对我们的方法和使用现有数据集进行广泛评估的详细分析，以支持所提出的方法。实施细节可在https://github.com/aislab/feat2map上获得
Camera relocalization involving a prior 3D reconstruction plays a crucial role in many mixed reality and robotics applications. Estimating the camera pose directly with respect to pre-built 3D models can be prohibitively expensive for several applications with limited storage and/or communication bandwidth. Although recent scene and absolute pose regression methods have become popular for efficient camera localization, most of them are computation-resource intensive and difficult to obtain a real-time inference with high accuracy constraints. This study proposes a simple scene regression method that requires only a multi-layer perceptron network for mapping scene coordinates to achieve accurate camera pose estimations. The proposed approach uses sparse descriptors to regress the scene coordinates, instead of a dense RGB image. The use of sparse features provides several advantages. First, the proposed regressor network is substantially smaller than those reported in previous studies. This makes our system highly efficient and scalable. Second, the pre-built 3D models provide the most reliable and robust 2D-3D matches. Therefore, learning from them can lead to an awareness of equivalent features and substantially improve the generalization performance. A detailed analysis of our approach and extensive evaluations using existing datasets are provided to support the proposed method. The implementation detail is available at https://github.com/aislab/feat2map
