B-spline
PulseAugur coverage of B-spline — every cluster mentioning B-spline across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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New method reconstructs 3D geometry for direct CAD and simulation use
Researchers have developed FORGE-SIM, a novel method for reconstructing 3D geometry from sparse RGB images that is directly compatible with CAD and simulation workflows. This approach optimizes a multi-patch B-spline bo…
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New PI-Splines Architecture Offers Stable Alternative for Physics-Informed Learning
Researchers have introduced Physics-Informed Splines (PI-Splines), a novel architecture for physics-informed learning that directly parametrizes unknown fields using B-spline expansions. This method offers advantages ov…
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New PIE-PINN framework enhances elastic property estimation from noisy data
Researchers have developed a Probabilistic Inverse Elasticity Physics-Informed Neural Network (PIE-PINN) framework designed to robustly estimate heterogeneous elastic properties from noisy and low-resolution displacemen…
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New research details 3D point cloud attribute compression via deep unrolling
A new research paper on arXiv introduces a method for compressing attributes of 3D point clouds. The approach utilizes a multi-resolution B-spline framework and a feed-forward network derived from rate-distortion optimi…
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DynFly framework enhances UAV navigation with continuous trajectory generation
Researchers have developed DynFly, a new framework designed to improve the continuous trajectory generation for unmanned aerial vehicles (UAVs) in urban environments. This system addresses the gap between high-level nav…
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New framework enhances medical image super-resolution with dual-prior learning
Researchers have developed a new framework called Dual-Prior Null-space Learning (DP-NSL) for arbitrary slice super-resolution in medical imaging. This method reconstructs isotropic volumes from anisotropic clinical acq…
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New metrics assess hardware inference complexity of Kolmogorov-Arnold Networks
A new paper introduces hardware-oriented metrics for evaluating the inference complexity of Kolmogorov-Arnold Networks (KANs). These metrics, including Real Multiplications (RM), Bit Operations (BOP), and Number of Addi…
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New KAN Frameworks and Variants Enhance Research and Efficiency
Researchers have developed KANLib, a new framework designed to streamline research on Kolmogorov-Arnold Networks (KANs) by unifying features from existing implementations like PyKAN, EfficientKAN, and FastKAN. Concurren…
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New 3D Trace World Model Enhances Scalable Robot Learning
Researchers have developed $\mu_0$, a novel world model for robotics that utilizes 3D interaction traces to predict the movement of salient objects and points. This approach bypasses the need for embodiment-specific act…
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Adaptive RBF-KAN improves efficiency with new kernels and data-driven shape parameters
Researchers have developed an enhanced version of Kolmogorov-Arnold Networks (KANs) called adaptive RBF-KAN, which improves computational efficiency and flexibility. This new approach replaces the fixed Gaussian radial …
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New 4D wire framework enables unified 3D geometric abstraction
Researchers have developed a novel framework for 3D geometric abstraction by utilizing a single, continuous 4D wire. This approach, parameterized as a B-spline with spatial coordinates and variable width, represents com…