Browse > Computer Vision > 3D > 3D Reconstruction

3D Reconstruction

50 papers with code · Computer Vision
Subtask of 3D

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Structure-From-Motion Revisited

CVPR 2016 colmap/colmap

Incremental Structure-from-Motion is a prevalent strategy for 3D reconstruction from unordered image collections.

3D RECONSTRUCTION

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

3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions

CVPR 2017 andyzeng/3dmatch-toolbox

To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions.

3D RECONSTRUCTION

On Learning 3D Face Morphable Model from In-the-wild Images

28 Aug 2018tranluan/Nonlinear_Face_3DMM

To address these problems, this paper proposes an innovative framework to learn a nonlinear 3DMM model from a large set of in-the-wild face images, without collecting 3D face scans.

3D RECONSTRUCTION FACE ALIGNMENT IMAGE GENERATION

Nonlinear 3D Face Morphable Model

CVPR 2018 tranluan/Nonlinear_Face_3DMM

As a classic statistical model of 3D facial shape and texture, 3D Morphable Model (3DMM) is widely used in facial analysis, e. g., model fitting, image synthesis.

3D RECONSTRUCTION FACE ALIGNMENT IMAGE GENERATION

Learning a Multi-View Stereo Machine

NeurIPS 2017 akar43/lsm

We thoroughly evaluate our approach on the ShapeNet dataset and demonstrate the benefits over classical approaches as well as recent learning based methods.

3D RECONSTRUCTION

Deep Level Sets: Implicit Surface Representations for 3D Shape Inference

21 Jan 2019LMescheder/Occupancy-Networks

This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"

3D RECONSTRUCTION

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

Learning Implicit Fields for Generative Shape Modeling

CVPR 2019 LMescheder/Occupancy-Networks

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 REPRESENTATION LEARNING SINGLE-VIEW 3D RECONSTRUCTION