4 research outputs found
Tensor4D : Efficient Neural 4D Decomposition for High-fidelity Dynamic Reconstruction and Rendering
We present Tensor4D, an efficient yet effective approach to dynamic scene
modeling. The key of our solution is an efficient 4D tensor decomposition
method so that the dynamic scene can be directly represented as a 4D
spatio-temporal tensor. To tackle the accompanying memory issue, we decompose
the 4D tensor hierarchically by projecting it first into three time-aware
volumes and then nine compact feature planes. In this way, spatial information
over time can be simultaneously captured in a compact and memory-efficient
manner. When applying Tensor4D for dynamic scene reconstruction and rendering,
we further factorize the 4D fields to different scales in the sense that
structural motions and dynamic detailed changes can be learned from coarse to
fine. The effectiveness of our method is validated on both synthetic and
real-world scenes. Extensive experiments show that our method is able to
achieve high-quality dynamic reconstruction and rendering from sparse-view
camera rigs or even a monocular camera. The code and dataset will be released
at https://liuyebin.com/tensor4d/tensor4d.html
A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts
This paper presents a multi-robot system for manufacturing personalized
medical stent grafts. The proposed system adopts a modular design, which
includes: a (personalized) mandrel module, a bimanual sewing module, and a
vision module. The mandrel module incorporates the personalized geometry of
patients, while the bimanual sewing module adopts a learning-by-demonstration
approach to transfer human hand-sewing skills to the robots. The human
demonstrations were firstly observed by the vision module and then encoded
using a statistical model to generate the reference motion trajectories. During
autonomous robot sewing, the vision module plays the role of coordinating
multi-robot collaboration. Experiment results show that the robots can adapt to
generalized stent designs. The proposed system can also be used for other
manipulation tasks, especially for flexible production of customized products
and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial
Informatics, Key words: modularity, medical device customization, multi-robot
system, robot learning, visual servoing, robot sewin