3D object recognition is the task of recognising objects from 3D data.
Note that there are related tasks you can look at, such as 3D Object Detection which have more leaderboards.
( Image credit: Look Further to Recognize Better )
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In this paper, we first analyse the data distributions and interaction of foreground and background, then propose the foreground-background separated monocular depth estimation (ForeSeE) method, to estimate the foreground depth and background depth using separate optimization objectives and depth decoders.
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.
Ranked #3 on 3D Object Recognition on ModelNet40
We study the problem of 3D object generation.
Ranked #3 on 3D Shape Classification on Pix3D