9,611 research outputs found
Cell contraction induces long-ranged stress stiffening in the extracellular matrix
Animal cells in tissues are supported by biopolymer matrices, which typically
exhibit highly nonlinear mechanical properties. While the linear elasticity of
the matrix can significantly impact cell mechanics and functionality, it
remains largely unknown how cells, in turn, affect the nonlinear mechanics of
their surrounding matrix. Here we show that living contractile cells are able
to generate a massive stiffness gradient in three distinct 3D extracellular
matrix model systems: collagen, fibrin, and Matrigel. We decipher this
remarkable behavior by introducing Nonlinear Stress Inference Microscopy
(NSIM), a novel technique to infer stress fields in a 3D matrix from nonlinear
microrheology measurement with optical tweezers. Using NSIM and simulations, we
reveal a long-ranged propagation of cell-generated stresses resulting from
local filament buckling. This slow decay of stress gives rise to the large
spatial extent of the observed cell-induced matrix stiffness gradient, which
could form a mechanism for mechanical communication between cells
KRF: Keypoint Refinement with Fusion Network for 6D Pose Estimation
Existing refinement methods gradually lose their ability to further improve
pose estimation methods' accuracy. In this paper, we propose a new refinement
pipeline, Keypoint Refinement with Fusion Network (KRF), for 6D pose
estimation, especially for objects with serious occlusion. The pipeline
consists of two steps. It first completes the input point clouds via a novel
point completion network. The network uses both local and global features,
considering the pose information during point completion. Then, it registers
the completed object point cloud with corresponding target point cloud by Color
supported Iterative KeyPoint (CIKP). The CIKP method introduces color
information into registration and registers point cloud around each keypoint to
increase stability. The KRF pipeline can be integrated with existing popular 6D
pose estimation methods, e.g. the full flow bidirectional fusion network, to
further improved their pose estimation accuracy. Experiments show that our
method outperforms the state-of-the-art method from 93.9\% to 94.4\% on
YCB-Video dataset and from 64.4\% to 66.8\% on Occlusion LineMOD dataset. Our
source code is available at https://github.com/zhanhz/KRF
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