3D Object Retrieval
7 papers with code • 2 benchmarks • 2 datasets
Source: He et al
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds.
MVTN exhibits clear performance gains in the tasks of 3D shape classification and 3D shape retrieval without the need for extra training supervision.
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.
A binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented.
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.
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.
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.