3D Shape Representation
40 papers with code • 0 benchmarks • 4 datasets
Image: MeshNet
Benchmarks
These leaderboards are used to track progress in 3D Shape Representation
Objectron
Dynamic FAUST
KeypointNet
Foot3D
Most implemented papers
Occupancy Networks: Learning 3D Reconstruction in Function Space
LMescheder/Occupancy-Networks
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CVPR 2019
With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity.
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Facebookresearch/deepsdf
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CVPR 2019
In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data.
Learning Implicit Fields for Generative Shape Modeling
czq142857/implicit-decoder
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CVPR 2019
We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes.
BSP-Net: Generating Compact Meshes via Binary Space Partitioning
czq142857/BSP-NET-original
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CVPR 2020
The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built on a set of planes.
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
lioryariv/idr
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NeurIPS 2020
In this work we address the challenging problem of multiview 3D surface reconstruction.
On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes
Many prior works have focused on _latent-encoded_ neural implicits, where a latent vector encoding of a specific shape is also fed as input.
3D ShapeNets: A Deep Representation for Volumetric Shapes
AliBahri94/SVWA_TTA
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CVPR 2015
Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically.
Learning Discriminative 3D Shape Representations by View Discerning Networks
In this network, a Score Generation Unit is devised to evaluate the quality of each projected image with score vectors.
MeshNet: Mesh Neural Network for 3D Shape Representation
iMoonLab/MeshNet
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However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data.
3D Point Capsule Networks
yongheng1991/3D-point-capsule-networks
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CVPR 2019
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
