论文标题
IDF-SLAM：端到端的RGB-D大满贯，带有神经隐性映射和深度功能跟踪
iDF-SLAM: End-to-End RGB-D SLAM with Neural Implicit Mapping and Deep Feature Tracking
论文作者
论文摘要
我们提出了一个新颖的端到端RGB-D SLAM，IDF-SLAM，它采用了基于功能的深神经跟踪器作为前端，而NERF风格的神经隐式映射器作为后端。神经隐式映射器经过训练，尽管神经跟踪器在扫描仪数据集中介绍了，但它在神经隐式映射器的培训中也受到了挑战。在这样的设计下，我们的IDF-SLAM能够学习使用特定于场景的功能进行相机跟踪，从而使SLAM系统的终身学习。在没有引入地面真相姿势的情况下，对追踪器的培训和映射者都进行了自我监督。我们测试了IDF-SLAM在副本和扫描数据集上的性能，并将结果与两个基于NERF的两个基于NERF的神经大满贯系统进行了比较。拟议的IDF-SLAM在相机跟踪中的场景重建和竞争性能方面展示了最先进的结果。
We propose a novel end-to-end RGB-D SLAM, iDF-SLAM, which adopts a feature-based deep neural tracker as the front-end and a NeRF-style neural implicit mapper as the back-end. The neural implicit mapper is trained on-the-fly, while though the neural tracker is pretrained on the ScanNet dataset, it is also finetuned along with the training of the neural implicit mapper. Under such a design, our iDF-SLAM is capable of learning to use scene-specific features for camera tracking, thus enabling lifelong learning of the SLAM system. Both the training for the tracker and the mapper are self-supervised without introducing ground truth poses. We test the performance of our iDF-SLAM on the Replica and ScanNet datasets and compare the results to the two recent NeRF-based neural SLAM systems. The proposed iDF-SLAM demonstrates state-of-the-art results in terms of scene reconstruction and competitive performance in camera tracking.
