We present a novel approach for the reconstruction of dynamic geometric
shapes using a single hand-held consumer-grade RGB-D sensor at real-time rates.
Our method does not require a pre-defined shape template to start with and
builds up the scene model from scratch during the scanning process. Geometry
and motion are parameterized in a unified manner by a volumetric representation
that encodes a distance field of the surface geometry as well as the non-rigid
space deformation. Motion tracking is based on a set of extracted sparse color
features in combination with a dense depth-based constraint formulation. This
enables accurate tracking and drastically reduces drift inherent to standard
model-to-depth alignment. We cast finding the optimal deformation of space as a
non-linear regularized variational optimization problem by enforcing local
smoothness and proximity to the input constraints. The problem is tackled in
real-time at the camera's capture rate using a data-parallel flip-flop
optimization strategy. Our results demonstrate robust tracking even for fast
motion and scenes that lack geometric features.

通过使用单个消费级 RGB-D 传感器以实时速率进行动态几何形状的重建，我们提出了一种新的方法，该方法不需要预定义的形状模板，并且在扫描过程中从头开始构建场景模型。采用了体积表示法对几何和运动进行参数化，并且基于提取的稀疏颜色特征与密集深度约束公式相结合实现了运动跟踪。从而保证了准确跟踪并大大降低了标准模型到深度对齐固有的漂移。同时，我们还将寻找空间最佳变形视为非线性正则化变分优化问题，并通过在本地平滑和靠近输入约束的同时实现了数据并行的翻转优化策略。研究结果表明，即使在快速运动和缺乏几何特征的场景下，也能实现稳健的跟踪。