3D object recognition is the task of recognising objects from 3D data.
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In this paper we propose a neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings.
We improve upon these methods by introducing a view clustering and pooling layer based on dominant sets.
In this study, we present an analysis of model-based ensemble learning for 3D point-cloud object classification and detection.
The multi-level voxel representation consists of a coarse voxel grid that contains volumetric information of the 3D object.
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.
3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object.
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
#3 best model for 3D Object Recognition on ModelNet40
We study the problem of 3D object generation.
Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations.
SOTA for 3D Object Recognition on ModelNet40