  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.
