3D Shape Recognition
13 papers with code • 0 benchmarks • 1 datasets
Image: Wei et al
These leaderboards are used to track progress in 3D Shape Recognition
LibrariesUse these libraries to find 3D Shape Recognition models and implementations
Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances.
With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance.
In this network, a Score Generation Unit is devised to evaluate the quality of each projected image with score vectors.
MVTN exhibits clear performance gains in the tasks of 3D shape classification and 3D shape retrieval without the need for extra training supervision.
View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition.
In this way, each 3D shape with arbitrary views is represented by a fixed number of canonical view features, which are further aggregated to generate a rich and robust 3D shape representation for shape recognition.
The fields of SocialVR, performance capture, and virtual try-on are often faced with a need to faithfully reproduce real garments in the virtual world.