3D Shape Representation
38 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.
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.
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.
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.
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.
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation
gbouritsas/Neural3DMM
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ICCV 2019
Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics.
