research
∙
08/29/2023
Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection
Unsupervised domain adaptation (UDA) plays a crucial role in object dete...
share
research
∙
07/28/2023
PatchMixer: Rethinking network design to boost generalization for 3D point cloud understanding
The recent trend in deep learning methods for 3D point cloud understandi...
research
∙
07/28/2023
Revisiting Fully Convolutional Geometric Features for Object 6D Pose Estimation
Recent works on 6D object pose estimation focus on learning keypoint cor...
research
∙
12/06/2022
Supervised Tractogram Filtering using Geometric Deep Learning
A tractogram is a virtual representation of the brain white matter. It i...
research
∙
05/21/2021
Generalisable and distinctive 3D local deep descriptors for point cloud registration
An effective 3D descriptor should be invariant to different geometric tr...
research
∙
09/01/2020
Distinctive 3D local deep descriptors
We present a simple but yet effective method for learning distinctive 3D...
research
∙
08/04/2020
Shape Consistent 2D Keypoint Estimation under Domain Shift
Recent unsupervised domain adaptation methods based on deep architecture...
research
∙
04/15/2020
Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges
Point cloud processing and 3D shape understanding are very challenging t...
research
∙
03/24/2020
Tractogram filtering of anatomically non-plausible fibers with geometric deep learning
Tractograms are virtual representations of the white matter fibers of th...
research
∙
02/02/2020
3D Shape Segmentation with Geometric Deep Learning
The semantic segmentation of 3D shapes with a high-density of vertices c...
research
∙
11/25/2016
Geometric deep learning on graphs and manifolds using mixture model CNNs
Deep learning has achieved a remarkable performance breakthrough in seve...
research
∙
05/20/2016
Learning shape correspondence with anisotropic convolutional neural networks
Establishing correspondence between shapes is a fundamental problem in g...
research
∙
01/26/2015
Geodesic convolutional neural networks on Riemannian manifolds
Feature descriptors play a crucial role in a wide range of geometry anal...
research
∙
06/07/2014
