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3D Shape Reconstruction

11 papers with code ·

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Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55

17 Oct 2017facebookresearch/SparseConvNet

We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.

3D RECONSTRUCTION 3D SHAPE RECONSTRUCTION

SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator

13 Nov 2019sw-gong/spiralnet_plus

Intrinsic graph convolution operators with differentiable kernel functions play a crucial role in analyzing 3D shape meshes.

3D SHAPE RECONSTRUCTION

Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion

3 Mar 2020jchibane/if-net

To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans.

3D OBJECT RECONSTRUCTION 3D RECONSTRUCTION 3D SHAPE RECONSTRUCTION

Multiview Aggregation for Learning Category-Specific Shape Reconstruction

NeurIPS 2019 drsrinathsridhar/xnocs

We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances.

3D SHAPE RECONSTRUCTION

PAI-GCN: Permutable Anisotropic Graph Convolutional Networks for 3D Shape Representation Learning

21 Apr 2020Gaozhongpai/PaiConvMesh

The recent success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to 3D shapes.

3D SHAPE RECONSTRUCTION 3D SHAPE REPRESENTATION REPRESENTATION LEARNING

Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods

23 Apr 2019JuliaInv/ParamLevelSet.jl

This method not only allows us to analytically and compactly represent the object, it also confers on us the ability to overcome calibration related noise that originates from inaccurate acquisition parameters.

3D SHAPE RECONSTRUCTION CALIBRATION

Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers

CVPR 2018 JeremyFisher/deep_level_sets

We scale this baseline to higher resolutions by proposing a memory-efficient shape encoding, which recursively decomposes a 3D shape into nested shape layers, similar to the pieces of a Matryoshka doll.

3D SHAPE RECONSTRUCTION