  We contribute a dense SLAM system that takes a live stream of depth images as
input and reconstructs non-rigid deforming scenes in real time, without
templates or prior models. In contrast to existing approaches, we do not
maintain any volumetric data structures, such as truncated signed distance
function (TSDF) fields or deformation fields, which are performance and memory
intensive. Our system works with a flat point (surfel) based representation of
geometry, which can be directly acquired from commodity depth sensors. Standard
graphics pipelines and general purpose GPU (GPGPU) computing are leveraged for
all central operations: i.e., nearest neighbor maintenance, non-rigid
deformation field estimation and fusion of depth measurements. Our pipeline
inherently avoids expensive volumetric operations such as marching cubes,
volumetric fusion and dense deformation field update, leading to significantly
improved performance. Furthermore, the explicit and flexible surfel based
geometry representation enables efficient tackling of topology changes and
tracking failures, which makes our reconstructions consistent with updated
depth observations. Our system allows robots to maintain a scene description
with non-rigidly deformed objects that potentially enables interactions with
dynamic working environments.
