Search Results for author:
Found 18 papers, 9 papers with code
FreeZe: Training-free zero-shot 6D pose estimation with geometric and vision foundation models
We also introduce a novel algorithm to solve ambiguous cases due to geometrically symmetric objects that is based on visual features.
Open-vocabulary object 6D pose estimation
We introduce the new setting of open-vocabulary object 6D pose estimation, in which a textual prompt is used to specify the object of interest.
Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection
Unsupervised domain adaptation (UDA) plays a crucial role in object detection when adapting a source-trained detector to a target domain without annotated data.
PatchMixer: Rethinking network design to boost generalization for 3D point cloud understanding
The recent trend in deep learning methods for 3D point cloud understanding is to propose increasingly sophisticated architectures either to better capture 3D geometries or by introducing possibly undesired inductive biases.
Revisiting Fully Convolutional Geometric Features for Object 6D Pose Estimation
Recent works on 6D object pose estimation focus on learning keypoint correspondences between images and object models, and then determine the object pose through RANSAC-based algorithms or by directly regressing the pose with end-to-end optimisations.
The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios
MONET is different from previous thermal drone datasets because it features multimodal data, including rural scenes captured with thermal cameras containing both person and vehicle targets, along with trajectory information and metadata.
Supervised Tractogram Filtering using Geometric Deep Learning
A tractogram is a virtual representation of the brain white matter.
Learning general and distinctive 3D local deep descriptors for point cloud registration
An effective 3D descriptor should be invariant to different geometric transformations, such as scale and rotation, robust to occlusions and clutter, and capable of generalising to different application domains.
Ranked #1 on
Point Cloud Registration
on ETH (trained on 3DMatch)
Distinctive 3D local deep descriptors
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 alignment.
Ranked #2 on
Point Cloud Registration
on ETH (trained on 3DMatch)
Shape Consistent 2D Keypoint Estimation under Domain Shift
Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex problems involving structured predictions (e. g. semantic segmentation, depth estimation).
Novel-View Human Action Synthesis
We first estimate the 3D mesh of the target body and transfer the rough textures from the 2D images to the mesh.
Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges
Point cloud processing and 3D shape understanding are very challenging tasks for which deep learning techniques have demonstrated great potentials.
Tractogram filtering of anatomically non-plausible fibers with geometric deep learning
The intuitive idea is to model a fiber as a point cloud and the goal is to investigate whether and how a geometric deep learning model might capture its anatomical properties.
3D Shape Segmentation with Geometric Deep Learning
The semantic segmentation of 3D shapes with a high-density of vertices could be impractical due to large memory requirements.
Geometric deep learning on graphs and manifolds using mixture model CNNs
Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.
Ranked #4 on
Document Classification
on Cora
Learning shape correspondence with anisotropic convolutional neural networks
Establishing correspondence between shapes is a fundamental problem in geometry processing, arising in a wide variety of applications.
Geodesic convolutional neural networks on Riemannian manifolds
Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation.
Shape-from-intrinsic operator
Shape-from-X is an important class of problems in the fields of geometry processing, computer graphics, and vision, attempting to recover the structure of a shape from some observations.
