论文标题
Orbeez-slam：带有ORB特征和NERF实现的映射的实时单眼大满贯
Orbeez-SLAM: A Real-time Monocular Visual SLAM with ORB Features and NeRF-realized Mapping
论文作者
论文摘要
高度期望可以通过视觉信号执行复杂任务并与人合作执行复杂任务的空间AI。为了实现这一目标，我们需要一个视觉大满贯，该大满贯可以轻松适应新场景而无需预训练，并为实时的下游任务生成密集的地图。由于其组件的内在限制，先前基于学习的和非学习的视觉大满贯都无法满足所有需求。在这项工作中，我们开发了一个名为Orbeez-slam的视觉大满贯，该作品成功地与隐式神经表示和视觉探测器合作以实现我们的目标。此外，Orbeez-Slam可以与单眼相机一起使用，因为它只需要RGB输入，从而广泛适用于现实世界。结果表明，我们的大满贯速度比强大的基线快800倍。代码链接：https：//github.com/marvinchung/orbeez-slam。
A spatial AI that can perform complex tasks through visual signals and cooperate with humans is highly anticipated. To achieve this, we need a visual SLAM that easily adapts to new scenes without pre-training and generates dense maps for downstream tasks in real-time. None of the previous learning-based and non-learning-based visual SLAMs satisfy all needs due to the intrinsic limitations of their components. In this work, we develop a visual SLAM named Orbeez-SLAM, which successfully collaborates with implicit neural representation and visual odometry to achieve our goals. Moreover, Orbeez-SLAM can work with the monocular camera since it only needs RGB inputs, making it widely applicable to the real world. Results show that our SLAM is up to 800x faster than the strong baseline with superior rendering outcomes. Code link: https://github.com/MarvinChung/Orbeez-SLAM.
