  This paper presents a collaborative implicit neural simultaneous localization
and mapping (SLAM) system with RGB-D image sequences, which consists of
complete front-end and back-end modules including odometry, loop detection,
sub-map fusion, and global refinement. In order to enable all these modules in
a unified framework, we propose a novel neural point based 3D scene
representation in which each point maintains a learnable neural feature for
scene encoding and is associated with a certain keyframe. Moreover, a
distributed-to-centralized learning strategy is proposed for the collaborative
implicit SLAM to improve consistency and cooperation. A novel global
optimization framework is also proposed to improve the system accuracy like
traditional bundle adjustment. Experiments on various datasets demonstrate the
superiority of the proposed method in both camera tracking and mapping.
