Multi-view learning (MVL) has attracted increasing attention and achieved great practical
success by exploiting complementary information of multiple features or modalities …

The reconstruction of 3D object from a single image is an important task in the field of
computer vision. In recent years, 3D reconstruction of single image using deep learning …

We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …

We present a simple yet powerful neural network that implicitly represents and renders 3D
objects and scenes only from 2D observations. The network models 3D geometries as a …

Many machine learning tasks such as multiple instance learning, 3D shape recognition, and
few-shot image classification are defined on sets of instances. Since solutions to such …

We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …

Recovering the 3D shape of an object from single or multiple images with deep neural
networks has been attracting increasing attention in the past few years. Mainstream works …

Recent work on the representation of functions on sets has considered the use of summation
in a latent space to enforce permutation invariance. In particular, it has been conjectured that …

Deep CNN-based methods have so far achieved the state of the art results in multi-view 3D
object reconstruction. Despite the considerable progress, the two core modules of these …

Recent volumetric 3D reconstruction methods can produce very accurate results, with
plausible geometry even for unobserved surfaces. However, they face an undesirable trade …