We propose a dense neural simultaneous localization and mapping (SLAM) approach for monocular RGBD input which anchors the features of a neural scene representation in a point cloud that is iteratively generated in an input-dependent data-driven manner. We demonstrate that both tracking and mapping can be performed with the same point-based neural scene representation by minimizing an RGBD-based re-rendering loss. In contrast to recent dense neural SLAM methods which anchor the scene features in a sparse grid, our point-based approach allows dynamically adapting the anchor point density to the information density of the input. This strategy reduces runtime and memory usage in regions with fewer details and dedicates higher point density to resolve fine details. Our approach performs either better or competitive to existing dense neural RGBD SLAM methods in tracking, mapping and rendering accuracy on the Replica, TUM-RGBD and ScanNet datasets. The source code is available at https://github.com/eriksandstroem/Point-SLAM.

翻译：摘要：我们提出一种针对单目RGBD输入的稠密神经同步定位与地图构建（SLAM）方法，该方法将神经场景表示的特征锚定在点云中，该点云以输入依赖的数据驱动方式迭代生成。我们证明，通过最小化基于RGBD的重渲染损失，可以使用相同的基于点的神经场景表示同时执行跟踪和建图。与近期将场景特征锚定在稀疏网格上的稠密神经SLAM方法不同，我们的基于点的方法允许动态调整锚点密度以适应输入的信息密度。该策略减少了细节较少区域的运行时间和内存使用，并分配更高点密度来解析精细细节。在Replica、TUM-RGBD和ScanNet数据集上，我们的方法在跟踪、建图和渲染精度方面优于或媲美现有稠密神经RGBD SLAM方法。源代码见 https://github.com/eriksandstroem/Point-SLAM。