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Greatest papers with code

DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation

CVPR 2019 Facebookresearch/deepsdf

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.

3D RECONSTRUCTION 3D SHAPE REPRESENTATION

Occupancy Networks: Learning 3D Reconstruction in Function Space

CVPR 2019 LMescheder/Occupancy-Networks

With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity.

3D RECONSTRUCTION 3D SHAPE REPRESENTATION

Learning Implicit Fields for Generative Shape Modeling

CVPR 2019 czq142857/implicit-decoder

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.

3D RECONSTRUCTION 3D SHAPE REPRESENTATION REPRESENTATION LEARNING SINGLE-VIEW 3D RECONSTRUCTION

MeshNet: Mesh Neural Network for 3D Shape Representation

28 Nov 2018iMoonLab/MeshNet

However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data.

3D SHAPE CLASSIFICATION 3D SHAPE REPRESENTATION

BSP-Net: Generating Compact Meshes via Binary Space Partitioning

CVPR 2020 czq142857/BSP-NET-original

The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built on a set of planes.

3D RECONSTRUCTION 3D SHAPE REPRESENTATION