  Embedded deformation nodes based formulation has been widely applied in
deformable geometry and graphical problems. Though being promising in stereo
(or RGBD) sensor based SLAM applications, it remains challenging to keep
constant speed in deformation nodes parameter estimation when model grows
larger. In practice, the processing time grows rapidly in accordance with the
expansion of maps. In this paper, we propose an approach to decouple nodes of
deformation graph in large scale dense deformable SLAM and keep the estimation
time to be constant. We observe that only partial deformable nodes in the graph
are connected to visible points. Based on this fact, sparsity of original
Hessian matrix is utilized to split parameter estimation in two independent
steps. With this new technique, we achieve faster parameter estimation with
amortized computation complexity reduced from O(n^2) to closing O(1). As a
result, the computation cost barely increases as the map keeps growing. Based
on our strategy, computational bottleneck in large scale embedded deformation
graph based applications will be greatly mitigated. The effectiveness is
validated by experiments, featuring large scale deformation scenarios.
