The large-scale propagation of human-human handover research has been hindered by two challenges.

We provide optimization time ( i. e ., training time in the bracket) and inference time of our method. Our method improves the state-of-the-art results while using much fewer parameters. The surfaces are reconstructed from point clouds with low noise (a) and high noise (b). Fig 2, we show the reconstructed surfaces on point clouds with different noise levels. A partially enlarged view is provided for each shape.

Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. The state-of-the-art methods rely on priors of fitting local surfaces learned from normal supervision.

Our primary focus is the identification of the latent structure in measurement models without parametric assumptions such as linearity or Gaussianity.

In this paper, we present Rotating Features, a generalization of complex-valued features to higher dimensions, and a new evaluation procedure for extracting objects from distributed representations.

With the rapidly increasing demand for oriented object detection, e.g. in autonomous driving and remote sensing, the recently proposed paradigm involving

SVT incorporates the scattering network utilizing the DTCWT for image decomposition into low and high-frequency components. Our primary focus is to analyze the low-frequency and high-frequency filter components to emphasize SVT's exceptional directional orientation capabilities.