论文标题
ESLAM：基于签名距离场的混合表示的有效密集的大满贯系统
ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields
论文作者
论文摘要
我们提出ESLAM，这是一种有效的隐式神经表示方法，用于同时定位和映射（SLAM）。 Eslam以未知的相机呈现RGB-D框架以依次的方式呈现，并逐步重建场景表示，同时估计场景中当前的相机位置。我们将神经辐射场（NERF）的最新进展纳入了大满贯系统，从而导致了一种有效且准确的密集视觉大满贯方法。我们的场景表示由多尺度轴对准的垂直特征平面和浅解码器组成，对于连续空间中的每个点，将插值特征解码为截断的签名距离字段（TSDF）和RGB值。我们在三个标准数据集（Replica，Scannet和Tum RGB-D）上进行的广泛实验表明，ESLAM提高了3D重建和摄像机对最先进的视觉SLAM方法的精度和摄像机定位，而最多可快10倍，并且不需要任何预先培训。
We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera poses in a sequential manner and incrementally reconstructs the scene representation while estimating the current camera position in the scene. We incorporate the latest advances in Neural Radiance Fields (NeRF) into a SLAM system, resulting in an efficient and accurate dense visual SLAM method. Our scene representation consists of multi-scale axis-aligned perpendicular feature planes and shallow decoders that, for each point in the continuous space, decode the interpolated features into Truncated Signed Distance Field (TSDF) and RGB values. Our extensive experiments on three standard datasets, Replica, ScanNet, and TUM RGB-D show that ESLAM improves the accuracy of 3D reconstruction and camera localization of state-of-the-art dense visual SLAM methods by more than 50%, while it runs up to 10 times faster and does not require any pre-training.
