3D Shape Retrieval

14 papers with code • 0 benchmarks • 2 datasets

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Most implemented papers

Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation

maxjiang93/DDSL ICLR 2019

It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).

MVTN: Multi-View Transformation Network for 3D Shape Recognition

ajhamdi/MVTN ICCV 2021

MVTN exhibits clear performance gains in the tasks of 3D shape classification and 3D shape retrieval without the need for extra training supervision.

Triplet-Center Loss for Multi-View 3D Object Retrieval

popcornell/keras-triplet-center-loss CVPR 2018

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.

Deep Learning for Hand Gesture Recognition on Skeletal Data

guillaumephd/deep_learning_hand_gesture_recognition IEEE FG 2018 2018

In this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model.

Equivariant Multi-View Networks

daniilidis-group/emvn ICCV 2019

Several popular approaches to 3D vision tasks process multiple views of the input independently with deep neural networks pre-trained on natural images, achieving view permutation invariance through a single round of pooling over all views.

A Topological Nomenclature for 3D Shape Analysis in Connectomics

donglaiw/ibexHelper 27 Sep 2019

Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation.

Unsupervised Deep Shape Descriptor With Point Distribution Learning

WordBearerYI/Unsupervised-Deep-Shape-Descriptor-with-Point-Distribution-Learning CVPR 2020

This paper proposes a novel probabilistic framework for the learning of unsupervised deep shape descriptors with point distribution learning.

RISA-Net: Rotation-Invariant Structure-Aware Network for Fine-Grained 3D Shape Retrieval

IGLICT/RisaNET 2 Oct 2020

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.

Joint Learning of 3D Shape Retrieval and Deformation

mikacuy/joint_learning_retrieval_deformation CVPR 2021

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.