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 with no code
View N-gram Network for 3D Object Retrieval
By doing so, spatial information across multiple views is captured, which helps to learn a discriminative global embedding for each 3D object.
Rethinking Loss Design for Large-scale 3D Shape Retrieval
In this paper, we propose the Collaborative Inner Product Loss (CIP Loss) to obtain ideal shape embedding that discriminative among different categories and clustered within the same class.
Learning a Disentangled Embedding for Monocular 3D Shape Retrieval and Pose Estimation
We propose a novel approach to jointly perform 3D shape retrieval and pose estimation from monocular images. In order to make the method robust to real-world image variations, e. g. complex textures and backgrounds, we learn an embedding space from 3D data that only includes the relevant information, namely the shape and pose.
Angular Triplet-Center Loss for Multi-view 3D Shape Retrieval
How to obtain the desirable representation of a 3D shape, which is discriminative across categories and polymerized within classes, is a significant challenge in 3D shape retrieval.
