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Found 9 papers, 3 papers with code
Iterative Feedback Network for Unsupervised Point Cloud Registration
In this paper, we propose a novel Iterative Feedback Network (IFNet) for unsupervised point cloud registration, in which the representation of low-level features is efficiently enriched by rerouting subsequent high-level features.
Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration
Specifically, we first incorporate the projected images from the point clouds and fuse the cross-modal features using the attention mechanism.
DBDNet:Partial-to-Partial Point Cloud Registration with Dual Branches Decoupling
Point cloud registration plays a crucial role in various computer vision tasks, and usually demands the resolution of partial overlap registration in practice.
Data-Driven Min-Max MPC for Linear Systems
Designing data-driven controllers in the presence of noise is an important research problem, in particular when guarantees on stability, robustness, and constraint satisfaction are desired.
GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network
Identifying causal relations among multi-variate time series is one of the most important elements towards understanding the complex mechanisms underlying the dynamic system.
Linear Data-Driven Economic MPC with Generalized Terminal Constraint
By employing a generalized terminal constraint with artificial equilibrium, the scheme does not require prior knowledge of the optimal equilibrium.
Less is More: Adaptive Curriculum Learning for Thyroid Nodule Diagnosis
Thyroid nodule classification aims at determining whether the nodule is benign or malignant based on a given ultrasound image.
Optimal Synthesis of Opacity-Enforcing Supervisors for Qualitative and Quantitative Specifications
We assume that the system has a "secret" that does not want to be revealed to the intruder.
Opacity Enforcing Supervisory Control using Non-deterministic Supervisors
Compared with the standard deterministic control mechanism, such a non-deterministic control mechanism can enhance the plausible deniability of the controlled system as the online control decision is a random realization and cannot be implicitly inferred from the control policy.
