169 citations · 307 across the 20 of their papers we have counts for
6 papers · 1 filter
Distinctive 3D local deep descriptors
Fabio Poiesi, Davide Boscaini
We present a simple but yet effective method for learning distinctive 3D local deep descriptors (DIPs) that can be used to register point clouds without requiring an initial alignm…
Shape Consistent 2D Keypoint Estimation under Domain Shift
Levi O. Vasconcelos, Massimiliano Mancini, Davide Boscaini +3
Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex pr…
Novel-View Human Action Synthesis
Mohamed Ilyes Lakhal, Davide Boscaini, Fabio Poiesi +2
Novel-View Human Action Synthesis aims to synthesize the movement of a body from a virtual viewpoint, given a video from a real viewpoint. We present a novel 3D reasoning to synthe…
Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges
Antonio Alliegro, Davide Boscaini, Tatiana Tommasi
Point cloud processing and 3D shape understanding are very challenging tasks for which deep learning techniques have demonstrated great potentials. Still further progresses are ess…
Tractogram filtering of anatomically non-plausible fibers with geometric deep learning
Pietro Astolfi, Ruben Verhagen, Laurent Petit +4
Tractograms are virtual representations of the white matter fibers of the brain. They are of primary interest for tasks like presurgical planning, and investigation of neuroplastic…
3D Shape Segmentation with Geometric Deep Learning
Davide Boscaini, Fabio Poiesi
The semantic segmentation of 3D shapes with a high-density of vertices could be impractical due to large memory requirements. To make this problem computationally tractable, we pro…