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We study the problem of 3D object generation.
Ranked #3 on 3D Shape Classification on Pix3D
In this paper we propose a neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings.
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.
Ranked #1 on 3D Object Recognition on ModelNet40
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.
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
Ranked #4 on 3D Object Recognition on ModelNet40
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.
The multi-level voxel representation consists of a coarse voxel grid that contains volumetric information of the 3D object.