3D Shape Retrieval
14 papers with code • 0 benchmarks • 2 datasets
These leaderboards are used to track progress in 3D Shape Retrieval
Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation
It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).
MVTN exhibits clear performance gains in the tasks of 3D shape classification and 3D shape retrieval without the need for extra training supervision.
Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning from Unlabeled Videos and 3D-Shape Retrieval
Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs.
Most existing 3D object recognition algorithms focus on leveraging the strong discriminative power of deep learning models with softmax loss for the classification of 3D data, while learning discriminative features with deep metric learning for 3D object retrieval is more or less neglected.
Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation.
This paper proposes a novel probabilistic framework for the learning of unsupervised deep shape descriptors with point distribution learning.
Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class, which requires shape descriptors to be capable of representing detailed geometric information to discriminate shapes with globally similar structures.
In fact, we use the embedding space to guide the shape pairs used to train the deformation module, so that it invests its capacity in learning deformations between meaningful shape pairs.