3D Object Retrieval
9 papers with code • 2 benchmarks • 2 datasets
Source: He et al
Benchmarks
These leaderboards are used to track progress in 3D Object Retrieval
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ModelNet
The RBO Dataset of Articulated Objects and Interactions
Latest papers
HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans
stefan-ainetter/SCANnotateDataset
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We present an automated and efficient approach for retrieving high-quality CAD models of objects and their poses in a scene captured by a moving RGB-D camera.
Automatically Annotating Indoor Images with CAD Models via RGB-D Scans
stefan-ainetter/SCANnotateDataset
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We present an automatic method for annotating images of indoor scenes with the CAD models of the objects by relying on RGB-D scans.
ROCA: Robust CAD Model Retrieval and Alignment from a Single Image
We present ROCA, a novel end-to-end approach that retrieves and aligns 3D CAD models from a shape database to a single input image.
Partial 3D Object Retrieval using Local Binary QUICCI Descriptors and Dissimilarity Tree Indexing
A complete pipeline is presented for accurate and efficient partial 3D object retrieval based on Quick Intersection Count Change Image (QUICCI) binary local descriptors and a novel indexing tree.
MVTN: Multi-View Transformation Network for 3D Shape Recognition
MVTN exhibits clear performance gains in the tasks of 3D shape classification and 3D shape retrieval without the need for extra training supervision.
RISA-Net: Rotation-Invariant Structure-Aware Network for Fine-Grained 3D Shape Retrieval
IGLICT/RisaNET
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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.
An Indexing Scheme and Descriptor for 3D Object Retrieval Based on Local Shape Querying
A binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented.
Adversarial Autoencoders for Compact Representations of 3D Point Clouds
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds.
Triplet-Center Loss for Multi-View 3D Object Retrieval
popcornell/keras-triplet-center-loss
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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.
