We present SplitFusion, a novel dense RGB-D SLAM framework that
simultaneously performs tracking and dense reconstruction for both rigid and
non-rigid components of the scene. SplitFusion first adopts deep learning based
semantic instant segmentation technique to split the scene into rigid or
non-rigid surfaces. The split surfaces are independently tracked via rigid or
non-rigid ICP and reconstructed through incremental depth map fusion.
Experimental results show that the proposed approach can provide not only
accurate environment maps but also well-reconstructed non-rigid targets, e.g.
the moving humans.

提出了一种新的基于深度学习的语义瞬间分割技术的 RGB-D SLAM 框架 SplitFusion，该框架可以同时对场景中的刚性和非刚性组件进行跟踪和密集重构，提供了精确的环境地图和重建的非刚性目标，例如移动的人类。

SplitFusion: 非刚性场景的同时跟踪和建图

SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes

One of the widespread solutions for non-rigid tracking has a nested-loop
structure: with Gauss-Newton to minimize a tracking objective in the outer
loop, and Preconditioned Conjugate Gradient (PCG) to solve a sparse linear
system in the inner loop. In this paper, we employ learnable optimizations to
improve tracking robustness and speed up solver convergence. First, we upgrade
the tracking objective by integrating an alignment data term on deep features
which are learned end-to-end through CNN. The new tracking objective can
capture the global deformation which helps Gauss-Newton to jump over local
minimum, leading to robust tracking on large non-rigid motions. Second, we
bridge the gap between the preconditioning technique and learning method by
introducing a ConditionNet which is trained to generate a preconditioner such
that PCG can converge within a small number of steps. Experimental results
indicate that the proposed learning method converges faster than the original
PCG by a large margin.

通过内嵌学习算法的方式，对非刚性追踪问题进行优化，提高动态物体跟踪稳健性和求解器的收敛速度，实现了利用 CNN 学习特征进行全局形变的捕捉和快速生成预条件矩阵，在计算复杂度和实验结果方面胜过原算法。