论文标题
平面图的相机摆姿势完善
Floorplan-Aware Camera Poses Refinement
论文作者
论文摘要
处理大型室内场景是一项具有挑战性的任务，因为扫描注册和摄像头轨迹估计方法会在随着时间的流逝中积累错误。结果，重建扫描的质量不足以用于某些应用，例如基于视觉的本地化和导航，其中正确的墙壁位置至关重要。 对于许多室内场景，存在一个技术平面图的图像，其中包含有关场景的几何形状和主要结构元素的信息，例如墙壁，隔板和门。我们认为这样的平面图是空间信息的有用来源，可以指导3D模型优化。 标准的RGB-D 3D重建管道由应用于RGB-D序列的跟踪模块和一个束调节（BA）模块，该模块采用了姿势的RGB-D序列，并纠正了相机姿势以提高一致性。我们提出了一种新颖的优化算法，扩展了传统的BA，该算法以平面图的形式利用了有关场景结构的先验知识。我们在Redwood数据集和自捕获数据上进行的实验表明，利用平面图提高了3D重建的准确性。
Processing large indoor scenes is a challenging task, as scan registration and camera trajectory estimation methods accumulate errors across time. As a result, the quality of reconstructed scans is insufficient for some applications, such as visual-based localization and navigation, where the correct position of walls is crucial. For many indoor scenes, there exists an image of a technical floorplan that contains information about the geometry and main structural elements of the scene, such as walls, partitions, and doors. We argue that such a floorplan is a useful source of spatial information, which can guide a 3D model optimization. The standard RGB-D 3D reconstruction pipeline consists of a tracking module applied to an RGB-D sequence and a bundle adjustment (BA) module that takes the posed RGB-D sequence and corrects the camera poses to improve consistency. We propose a novel optimization algorithm expanding conventional BA that leverages the prior knowledge about the scene structure in the form of a floorplan. Our experiments on the Redwood dataset and our self-captured data demonstrate that utilizing floorplan improves accuracy of 3D reconstructions.
